docid: zacijchy

title: Pulse oximetry in low-resource settings during the COVID-19 pandemic


The Society of Radiographers 1920 to 2020()


Let's not forget the basics


Reckoning with mortality: global health, HIV, and the politics of data


docid: etaobgtl

title: Azithromycin/hydroxychloroquine: QT prolongation with a notched broad T-wave following off-label therapy: case report


Multiple drugs: Bacillus cereus bacteraemia and acute renal failure: case report


EMA issues recommendations on impurities in medicines


Nivolumab: Paradoxical immunologic response in the form of COVID-19: case report


Multiple drugs: COVID-19 infection: 4 case reports


Alteplase/heparin/low-molecular-weight-heparins: Lack of efficacy, femoral haemorrhage and jugular haemorrhage: 5 case reports


Monitoring patient–ventilator interaction by an end-expiratory occlusion maneuver


Enoxaparin sodium: Traumatic intracranial bleeding: case report


Etanercept/methotrexate: COVID-19: case report


title: Complement levels in patients with bloodstream infection due to Staphylococcus aureus or Gram-negative bacteria

text: The complement system is a vital component of the innate immune system, though its role in bacteremia is poorly understood. We present complement levels in Staphylococcus aureus bacteremia (SAB) and Gram-negative bacteremia (GNB) and describe observed associations of complement levels with clinical outcomes. Complement and cytokine levels were measured in serum samples from 20 hospitalized patients with SAB, 20 hospitalized patients with GNB, 10 non-infected hospitalized patients, and 10 community controls. C5a levels were significantly higher in patients with SAB as compared to patients with GNB. Low C4 and C3 levels were associated with septic shock and 30-day mortality in patients with GNB, and elevated C3 was associated with a desirable outcome defined as absence of (1) septic shock, (2) acute renal failure, and (3) death within 30 days of bacteremia. Low levels of C9 were associated with septic shock in patients with GNB but not SAB. Elevated IL-10 was associated with increased 30-day mortality in patients with SAB. Complement profiles differ in patients with SAB and those with GNB. Measurement of IL-10 in patients with SAB and of C4, C3, and C9 in patients with GNB may help to identify those at higher risk for poor outcomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10096-020-03955-z) contains supplementary material, which is available to authorized users.


docid: cpkmdhup

title: Hydroxychloroquine: Corrected QT interval prolongation, first degree atrioventricular block and left bundle branch block during an off-label treatment with hydroxychloroquine for COVID-19 pneumonia: 3 case reports


Risperidone: Hypothermia, bradycardia and cardiac arrest: 2 case reports


Hydrochlorothiazide: Hypocalcaemia: case report


Azithromycin/hydroxychloroquine/propofol: Hypoglycaemia and cardiotoxicities following off label use: 2 case reports


title: Does Community Size Matter in the Settlement Process? The Experience of Syrian Refugees in Lethbridge in Alberta, Canada

text: The article documents refugee experience in the first year of their settlement in a small city in Canada and then explores whether the size of the community matters in the settlement processes. This is based on an extensive study of Syrian refugee settlement experiences in one large Canadian city (Edmonton) and one small Canadian city (Lethbridge). The findings argue that contrary to existing scholarly literature, small municipalities such as Lethbridge were more creative, nimble, and efficient in settling Syrian newcomers. In small cities, however, lack of denominational and non-denominational organizations to complement government assistance, the onset of compassionate fatigue among the city dwellers, and limited retention of newcomers due to limited employment opportunity are real threats to the settlement process immediately after newcomers’ arrival and in the long term. A more substantial role of municipal governments in the refugee resettlement process is recommended to offset the disproportionate burden settlement sector carries.


Antivirals/direct oral anticoagulants interaction: Anticoagulation drug level increased and off label use: 12 case reports


Hydroxychloroquine: QTc prolongation following off-label-use: case report


Antineoplastics: COVID-19 infections: 4 case reports


Ramipril: Aggravation of liver toxicity: case report


Anesthesiology and the Anesthesiologists at COVID-19()


Winter Is Here: A Case Study in Updating the Neuroradiology Didactic Curriculum Through a Gamification of Thrones Solution


title: Early Detection of Wheel-Spinning in ASSISTments

text: Persistence is a crucial trait for learners. However, a common issue in mastery learning is that persistence is not always productive, a construct termed wheel-spinning. In this paper, we extend on prior work to develop wheel-spinning detectors in the ASSISTments learning system that distinguish between non-persistence, productive persistence and wheel-spinning. To understand how quickly we can detect each state, we use data from different numbers of practice opportunities and compare model performance across student-problem set pairs. We identify that a model constructed using data from the first nine practice opportunities outperforms models using less practice data. However, it is possible to differentiate students who will eventually wheel-spin from learners who will persist productively using data from only the first three opportunities. Wheel-spinning can be differentiated from non-persistence from the first five opportunities, and non-persistence can be differentiated from productive persistence from the first seven opportunities. These results show that early differentiation between wheel-spinning and productive persistence is feasible. These detectors relied upon hint requests, the correctness of prior opportunities, and the amount of practice and time on the skill. Identifying predictive features offer insights into the impact of in-system behaviors on wheel-spinning and guide the system design.


title: Evaluating Crowdsourcing and Topic Modeling in Generating Knowledge Components from Explanations

text: Associating assessment items with hypothesized knowledge components (KCs) enables us to gain fine-grained data on students’ performance within an ed-tech system. However, creating this association is a time consuming process and requires substantial instructor effort. In this study, we present the results of crowdsourcing valuable insights into the underlying concepts of problems in mathematics and English writing, as a first step in leveraging the crowd to expedite the task of generating KCs. We presented crowdworkers with two problems in each domain and asked them to provide three explanations about why one problem is more challenging than the other. These explanations were then independently analyzed through (1) a series of qualitative coding methods and (2) several topic modeling techniques, to compare how they might assist in extracting KCs and other insights from the participant contributions. Results of our qualitative coding showed that crowdworkers were able to generate KCs that approximately matched those generated by domain experts. At the same time, the topic models’ outputs were evaluated against both the domain expert generated KCs and the results of the previous coding to determine effectiveness. Ultimately we found that while the topic modeling was not up to parity with the qualitative coding methods, it did assist in identifying useful clusters of explanations. This work demonstrates a method to leverage both the crowd’s knowledge and topic modeling to assist in the process of generating KCs for assessment items.


title: LIWCs the Same, Not the Same: Gendered Linguistic Signals of Performance and Experience in Online STEM Courses

text: Women are traditionally underrepresented in science, technology, engineering, and mathematics (STEM). While the representation of women in STEM classrooms has grown rapidly in recent years, it remains pedagogically meaningful to understand whether their learning outcomes are achieved in different ways than male students. In this study, we explored this issue through the lens of language in the context of an asynchronous online discussion forum. We applied Linguistic Inquiry and Word Count (LIWC) to examine linguistic features of students’ reflective posting in an online chemistry class at a four-year university. Our results suggest that cognitive linguistic features significantly predict the likelihood of passing the course and increases perceived sense of belonging. However, these results only hold true for female students. Pronouns and words relevant to social presence correlate with passing the course in different directions, and this mixed relationship is more polarized among male students. Interestingly, the linguistic features per se do not differ significantly between genders. Overall, our findings provide a more nuanced account of the relationship between linguistic signals of social/cognitive presence and learning outcomes. We conclude with implications for pedagogical interventions and system design to inclusively support learner success in online STEM courses.


title: Making Sense of Student Success and Risk Through Unsupervised Machine Learning and Interactive Storytelling

text: This paper presents an interactive AI system to enable academic advisors and program leadership to understand the patterns of behavior related to student success and risk using data collected from institutional databases. We have worked closely with advisors in our development of an innovative temporal model of student data, unsupervised k-means algorithm on the data, and interactive user experiences with the data. We report on the design and evaluation of FIRST, Finding Interesting stoRies about STudents, that provides an interactive experience in which the advisor can: select relevant student features to be included in a temporal model, interact with a visualization of unsupervised learning that present patterns of student behavior and their correlation with performance, and to view automatically generated stories about individual students based on student data in the temporal model. We have developed a high fidelity prototype of FIRST using 10 years of student data in our College. As part of our iterative design process, we performed a focus group study with six advisors following a demonstration of the prototype. Our focus group evaluation highlights the sensemaking value in the temporal model, the unsupervised clusters of the behavior of all students in a major, and the stories about individual students.


title: Helping Teachers Help Their Students: A Human-AI Hybrid Approach

text: There is a global interest in artificial intelligence to support online learning, but little increase in support for online professors, teachers and tutors (instructors). Over time, more students join online learning, but instructors have no equivalent increase in support to manage their online classes, leaving students under-served. This is evidenced by the number of students who dropout or fail online courses, blaming the “lack of support” from instructors. Interactions in such courses generate considerable quantity and diversity of data, allowing the extraction of pedagogically relevant information. However, instructors do not master the techniques and technologies needed to do it, and it is not practical to train them to do so. In this work, we propose an authoring tool (called T-Partner) that implements a process we created to deal with educational data. The objective is to support instructors making informed pedagogical decisions to manage their online course. T-Partner promotes the cooperation between artificial and human intelligences, however we do not know the appropriate balance between these “intelligences”. We then created two versions of the T-Partner to help instructors to: (1) find relevant pedagogical situations occurring within their online courses; (2) understand these situations; (3) create interventions (study plans, for example) to address these situations; (4) monitor and evaluate the impact of these interventions. We evaluated if both versions allowed instructors to make pedagogical decisions and their perceptions regarding this support to decision-making. The results show that both versions brought benefits to pedagogical decision-making, and were positively perceived by the participants.


title: DETECT: A Hierarchical Clustering Algorithm for Behavioural Trends in Temporal Educational Data

text: Techniques for clustering student behaviour offer many opportunities to improve educational outcomes by providing insight into student learning. However, one important aspect of student behaviour, namely its evolution over time, can often be challenging to identify using existing methods. This is because the objective functions used by these methods do not explicitly aim to find cluster trends in time, so these trends may not be clearly represented in the results. This paper presents ‘DETECT’ (Detection of Educational Trends Elicited by Clustering Time-series data), a novel divisive hierarchical clustering algorithm that incorporates temporal information into its objective function to prioritise the detection of behavioural trends. The resulting clusters are similar in structure to a decision tree, with a hierarchy of clusters defined by decision rules on features. DETECT is easy to apply, highly customisable, applicable to a wide range of educational datasets and yields easily interpretable results. Through a case study of two online programming courses ([Formula: see text] ), this paper demonstrates two example applications of DETECT: 1) to identify how cohort behaviour develops over time and 2) to identify student behaviours that characterise exercises where many students give up.


title: Neural Multi-task Learning for Teacher Question Detection in Online Classrooms

text: Asking questions is one of the most crucial pedagogical techniques used by teachers in class. It not only offers open-ended discussions between teachers and students to exchange ideas but also provokes deeper student thought and critical analysis. Providing teachers with such pedagogical feedback will remarkably help teachers improve their overall teaching quality over time in classrooms. Therefore, in this work, we build an end-to-end neural framework that automatically detects questions from teachers’ audio recordings. Compared with traditional methods, our approach not only avoids cumbersome feature engineering, but also adapts to the task of multi-class question detection in real education scenarios. By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions. We conducted extensive experiments on the question detection tasks in a real-world online classroom dataset and the results demonstrate the superiority of our model in terms of various evaluation metrics.


title: Can Ontologies Support the Gamification of Scripted Collaborative Learning Sessions?

text: In the field of Computer-Supported Collaborative Learning (CSCL), scripts orchestrate the collaborative learning (CL) process to achieve meaningful interactions among the students and so improve the learning outcomes. Nevertheless, the use of scripts may cause motivational problems over time. To deal with this issue, we propose the gamification of scripted CL sessions through an ontology that encodes knowledge from game design practices and theories of motivation and human behavior. This knowledge may be used by intelligent theory-aware systems to avoid the one-size-fits-all approach, providing support for the personalization of gamification. In this paper, we reported the results obtained in an empirical study to validate our ontology-based gamification of scripted CL sessions. Findings from this study indicate that intrinsic motivation, perceived choice, and effort/importance of students were significantly better when our ontology was used to support the gamification. The learning outcomes were significantly better in scripted CL sessions gamified through our approach, with positive correlations to the intrinsic motivation and perceived choice. Based on these results, we can state that the use of ontologies provides adequate support to carry out well-thought-out gamification of scripted sessions.


title: Robust Neural Automated Essay Scoring Using Item Response Theory

text: Automated essay scoring (AES) is the task of automatically assigning scores to essays as an alternative to human grading. Conventional AES methods typically rely on manually tuned features, which are laborious to effectively develop. To obviate the need for feature engineering, many deep neural network (DNN)-based AES models have been proposed and have achieved state-of-the-art accuracy. DNN-AES models require training on a large dataset of graded essays. However, assigned grades in such datasets are known to be strongly biased due to effects of rater bias when grading is conducted by assigning a few raters in a rater set to each essay. Performance of DNN models rapidly drops when such biased data are used for model training. In the fields of educational and psychological measurement, item response theory (IRT) models that can estimate essay scores while considering effects of rater characteristics have recently been proposed. This study therefore proposes a new DNN-AES framework that integrates IRT models to deal with rater bias within training data. To our knowledge, this is a first attempt at addressing rating bias effects in training data, which is a crucial but overlooked problem.


title: Modeling the Relationships Between Basic and Achievement Emotions in Computer-Based Learning Environments

text: Commercial facial affect detection software is typically trained on large databases and achieves high accuracy in detecting basic emotions, but their use in educational settings is unclear. The goal of this research is to determine how basic emotions relate to the achievement emotion states that are more relevant in academic settings. Such relations, if accurate and consistent, may be leveraged to make more effective use of the commercial affect-detection software. For this study, we collected affect data over four days from a classroom study with 65 students using Betty’s Brain. Basic emotions obtained from commercial software were aligned to achievement emotions obtained using sensor-free models. Interpretable classifiers enabled the study of relationships between the two types of emotions. Our findings show that certain basic emotions can help infer complex achievement emotions such as confusion, frustration and engaged concentration. This suggests the possibility of using commercial software as a less context-sensitive and more development-friendly alternative to the affect detector models currently used in learning environments.


title: Detecting Off-Task Behavior from Student Dialogue in Game-Based Collaborative Learning

text: Collaborative game-based learning environments integrate game-based learning and collaborative learning. These environments present students with a shared objective and provide them with a means to communicate, which allows them to share information, ask questions, construct explanations, and work together toward their shared goal. A key challenge in collaborative learning is that students may engage in unproductive discourse, which may affect learning activities and outcomes. Collaborative game-based learning environments that can detect this off-task behavior in real-time have the potential to enhance collaboration between students by redirecting the conversation back to more productive topics. This paper investigates the use of dialogue analysis to classify student conversational utterances as either off-task or on-task. Using classroom data collected from 13 groups of four students, we trained off-task dialogue models for text messages from a group chat feature integrated into Crystal Island: EcoJourneys, a collaborative game-based learning environment for middle school ecosystem science. We evaluate the effectiveness of the off-task dialogue models, which use different word embeddings (i.e., word2vec, ELMo, and BERT), as well as predictive off-task dialogue models that capture varying amounts of contextual information from the chat log. Results indicate that predictive off-task dialogue models that incorporate a window of recent context and represent the sequential nature of the chat messages achieve higher predictive performance compared to models that do not leverage this information. These findings suggest that off-task dialogue models for collaborative game-based learning environments can reliably recognize and predict students’ off-task behavior, which introduces the opportunity to adaptively scaffold collaborative dialogue.


title: Studying the Interactions Between Science, Engineering, and Computational Thinking in a Learning-by-Modeling Environment

text: Computational Thinking (CT) can play a central role in fostering students’ integrated learning of science and engineering. We adopt this framework to design and develop the Water Runoff Challenge (WRC) curriculum for lower middle school students in the USA. This paper presents (1) the WRC curriculum implemented in an integrated computational modeling and engineering design environment and (2) formative and summative assessments used to evaluate learner’s science, engineering, and CT skills as they progress through the curriculum. We derived a series of performance measures associated with student learning from system log data and the assessments. By applying Path Analysis we found significant relations between measures of science, engineering, and CT learning, indicating that they are mutually supportive of learning across these disciplines.


title: Investigating Visitor Engagement in Interactive Science Museum Exhibits with Multimodal Bayesian Hierarchical Models

text: Engagement plays a critical role in visitor learning in museums. Devising computational models of visitor engagement shows significant promise for enabling adaptive support to enhance visitors’ learning experiences and for providing analytic tools for museum educators. A salient feature of science museums is their capacity to attract diverse visitor populations that range broadly in age, interest, prior knowledge, and socio-cultural background, which can significantly affect how visitors interact with museum exhibits. In this paper, we introduce a Bayesian hierarchical modeling framework for predicting learner engagement with Future Worlds, a tabletop science exhibit for environmental sustainability. We utilize multi-channel data (e.g., eye tracking, facial expression, posture, interaction logs) captured from visitor interactions with a fully-instrumented version of Future Worlds to model visitor dwell time with the exhibit in a science museum. We demonstrate that the proposed Bayesian hierarchical modeling approach outperforms competitive baseline techniques. These findings point toward significant opportunities for enriching our understanding of visitor engagement in science museums with multimodal learning analytics.


docid: qbcwvetx

title: Real-Time Multimodal Feedback with the CPR Tutor

text: We developed the CPR Tutor, a real-time multimodal feedback system for cardiopulmonary resuscitation (CPR) training. The CPR Tutor detects mistakes using recurrent neural networks for real-time time-series classification. From a multimodal data stream consisting of kinematic and electromyographic data, the CPR Tutor system automatically detects the chest compressions, which are then classified and assessed according to five performance indicators. Based on this assessment, the CPR Tutor provides audio feedback to correct the most critical mistakes and improve the CPR performance. To test the validity of the CPR Tutor, we first collected the data corpus from 10 experts used for model training. Hence, to test the impact of the feedback functionality, we ran a user study involving 10 participants. The CPR Tutor pushes forward the current state of the art of real-time multimodal tutors by providing: 1) an architecture design, 2) a methodological approach to design multimodal feedback and 3) a field study on real-time feedback for CPR training.


title: Affective Sequences and Student Actions Within Reasoning Mind

text: Now that the modeling of affective states is beginning to mature, understanding affect dynamics has become an increasingly realistic endeavor. However, the results from empirical studies have not always matched those of theoretical models, which raises questions as to why. In this study, we explore the relationship between affective sequences that have been previously explored in the literature and the activities students may engage in when interacting with Reasoning Mind, a blended learning system for elementary mathematics. The strongest correlations are found for students who shift from engaged concentration to frustration, making fewer actions in the system. While confusion is generally associated with positive patterns, and frustration and boredom have unexpectedly similar implications for student activity.


docid: vzzqaqky

title: SoundHunters: Increasing Learner Phonological Awareness in Plains Cree

text: Indigenous languages have been dying out due to colonial practices that limited and even punished their use. For this reason, there is a need to support the maintenance and revitalization of these languages as part of the reconciliation process. However, there has been little research to guide the use of technology in supporting language revitalization. To contribute to this process, this study investigated the use of a novel e-learning activity for a specific Indigenous language - Plains Cree (nehiyawewin). This activity, SoundHunters, targets the development of learner phonological awareness (i.e., their ability to understand and manipulate sounds in a language) through game play. A mixed-methods study was used to measure learning and explore learner experiences. Learner performance on a transcription task, which required the mapping of sounds to characters, improved following SoundHunters use. The nature of learner errors indicates the development of learners’ interlanguage and provides evidence of transfer from English to Cree. Additionally, learners enjoyed the activity while finding it appropriately challenging. These results show the potential for using adaptive technology to support learning in low-resource settings, such as those that exist for most Indigenous languages.


title: Exploring Automated Question Answering Methods for Teaching Assistance

text: One important aspect of learning is through verbal interactions with teachers or teaching assistants (TAs), which requires significant effort and puts a heavy burden on teachers. Artificial intelligence has the potential to reduce their burden by automatically addressing the routine part of this interaction, which will free them up to focus on more important aspects of learning. We explore the use of automated question answering methods to power virtual TAs in online course discussion forums, which are heavily relied on during the COVID-19 pandemic as classes transition online. First, we focus on answering frequent and repetitive logistical questions and adopt a question answering framework that consists of two steps: retrieving relevant documents from a repository and extracting answers from retrieved documents. The document repository consists of course materials that contain information on course logistics, e.g., the syllabus, lecture slides, course emails, and prior discussion forum posts. This question answering framework can help virtual TAs decide whether a question is answerable and how to answer it. Second, we analyze the timing of student posts in discussion threads and develop a classifier to predict the timing of follow-up posts. This classifier can help virtual TAs decide whether to respond to a question and when to do so. We conduct experiments on data collected from an introductory physics course and discuss both the utility and limitations of our approach .


title: A Conceptual Framework for Human–AI Hybrid Adaptivity in Education

text: Educational AI (AIEd) systems are increasingly designed and evaluated with an awareness of the hybrid nature of adaptivity in real-world educational settings. In practice, beyond being a property of AIEd systems alone, adaptivity is often jointly enacted by AI systems and human facilitators (e.g., teachers or peers). Despite much recent research activity, theoretical and conceptual guidance for the design of such human–AI systems remains limited. In this paper we explore how adaptivity may be shared across AIEd systems and the various human stakeholders who work with them. Based on a comparison of prior frameworks, which tend to examine adaptivity in AIEd systems or human coaches separately, we first synthesize a set of dimensions general enough to capture human–AI hybrid adaptivity. Using these dimensions, we then present a conceptual framework to map distinct ways in which humans and AIEd systems can augment each other’s abilities. Through examples, we illustrate how this framework can be used to characterize prior work and envision new possibilities for human–AI hybrid approaches in education.


title: Using Neural Tensor Networks for Open Ended Short Answer Assessment

text: In this paper, we present a novel approach to leverage the power of Neural Tensor Networks (NTN) for student answer assessment in intelligent tutoring systems. The approach was evaluated on data collected using a dialogue based intelligent tutoring system (ITS). Particularly, we have experimented with different assessment models that were trained using features generated from knowledge graph embeddings derived with NTN. Our experiments showed that the model trained with the feature vectors generated with NTN, when trained with a combination of domain specific and domain general triplets, performs better than a previously proposed LSTM based approach.


title: Supporting Teacher Assessment in Chinese Language Learning Using Textual and Tonal Features

text: Assessment in the context of foreign language learning can be difficult and time-consuming for instructors. Distinctive from other domains, language learning often requires teachers to assess each student’s ability to speak the language, making this process even more time-consuming in large classrooms which are particularly common in post-secondary settings; considering that language instructors often assess students through assignments requiring recorded audio, a lack of tools to support such teachers makes providing individual feedback even more challenging. In this work, we seek to explore the development of tools to automatically assess audio responses within a college-level Chinese language-learning course. We build a model designed to grade student audio assignments with the purpose of incorporating such a model into tools focused on helping both teachers and students in real classrooms. Building upon our prior work which explored features extracted from audio, the goal of this work is to explore additional features derived from tone and speech recognition models to help assess students on two outcomes commonly observed in language learning classes: fluency and accuracy of speech. In addition to the exploration of features, this work explores the application of Siamese deep learning models for this assessment task. We find that models utilizing tonal features exhibit higher predictive performance of student fluency while text-based features derived from speech recognition models exhibit higher predictive performance of student accuracy of speech.


title: Analysis of Task Difficulty Sequences in a Simulation-Based POE Environment

text: Task difficulty (TD) reflects students’ subjective judgement on the complexity of a task. We examine the task difficulty sequence data of 236 undergraduate students in a simulation-based Predict-Observe-Explain environment. The findings suggest that if students perceive the TDs as easy or hard, it may lead to poorer learning outcomes, while the medium or moderate TDs may result in better learning outcomes. In terms of TD transitions, difficulty level hard followed by a hard may lead to poorer learning outcomes. By contrast, difficulty level medium followed by a medium may lead to better learning outcomes. Understanding how task difficulties manifest over time and how they impact students’ learning outcomes is useful, especially when designing for real-time educational interventions, where the difficulty of the tasks could be optimised for students. It can also help in designing and sequencing the tasks for the development of effective teaching strategies that can maximize students’ learning.


title: Using Motion Sensors to Understand Collaborative Interactions in Digital Fabrication Labs

text: Open-ended learning environments such as makerspaces present a unique challenge for instructors. While it is expected that students are given free rein to work on their projects, facilitators have to strike a difficult balance between micromanaging them and letting the community support itself. In this paper, we explore how Kinect sensors can continuously monitor students’ collaborative interactions so that instructors can gain a more comprehensive view of the social dynamics of the space. We employ heatmaps to examine the diversity of student collaborative interactions and Markov transition probabilities to explore the transitions between instances of collaborative interactions. Findings indicate that letting students work on their own promotes the development of technical skills, while working together encourages students to spend more time in the makerspace. This confirms the intuition that successful projects in makerspaces necessitate both individual and group efforts. Furthermore, such aggregation and display of information can aid instructors in uncovering the state of student learning in makerspaces. Identifying the instances and diversity of collaborative interactions affords instructors an early opportunity to identify struggling students and having these data in a near real-time manner opens new doors in terms of making (un)productive behaviors salient, both for teachers and students. We discuss how this work represents a first step toward using intelligent systems to support student learning in makerspaces.


docid: gbzyueax

title: Impact of Conversational Formality on the Quality and Formality of Written Summaries

text: This study investigated the impact of conversational agent formality on the quality of summaries and formality of written summaries during the training session and on posttest in a trialog-based intelligent tutoring system (ITS). During training, participants learned summarization strategies with the guidance of conversational agents who spoke one of the following three styles of language: (1) a formal language for both the teacher agent and the student agent, (2) an informal language for both agents, and (3) a mixed language with a formal language for the teacher agent and the informal language for the student agent. Results showed that participants wrote better quality summaries during training than pretest and/or posttest in each condition. Results also showed that agent informal language caused participants to write more informal summaries during training than on pretest. Implications are discussed for the potential application of adaptive design of conversational agents in the ITS.


title: The Sound of Inattention: Predicting Mind Wandering with Automatically Derived Features of Instructor Speech

text: Lecturing in a classroom environment is challenging - instructors are tasked with maintaining students’ attention for extended periods of time while they are speaking. Previous work investigating the influence of speech on attention, however, has not yet been extended to instructor speech in live classroom lectures. In the current study, we automatically extracted acoustic features from live lectures to determine their association with rates of classroom mind-wandering (i.e., lack of student attention). Results indicated that five speech features reliably predicted classroom mind-wandering rates (Harmonics-to-Noise Ratio, Formant 1 Mean, Formant 2 Mean, Formant 3 Mean, and Jitter Standard Deviation). These speaker correlates of mind-wandering may be a foundation for developing a system to provide feedback in real-time for lecturers online and in the classroom. Such a system may prove to be highly beneficial in developing real-time tools to retain student attention, as well as informing other applications outside of the classroom.


title: Investigating Differential Error Types Between Human and Simulated Learners

text: Simulated learners represent computational theories of human learning that can be used to evaluate educational technologies, provide practice opportunities for teachers, and advance our theoretical understanding of human learning. A key challenge in working with simulated learners is evaluating the accuracy of the simulation compared to the behavior of real human students. One way this evaluation is done is by comparing the error-rate learning curves from a population of human learners and a corresponding set of simulated learners. In this paper, we argue that this approach misses an opportunity to more accurately capture nuances in learning by treating all errors as the same. We present a simulated learner system, the Apprentice Learner (AL) Architecture, and use this more nuanced evaluation to demonstrate ways in which it does and does not explain and accurately predict student learning in terms of the reduction of different kinds of errors over time as it learns, as human students do, from an Intelligent Tutoring System (ITS).


title: Impact of Methodological Choices on the Evaluation of Student Models

text: The evaluation of student models involves many methodological decisions, e.g., the choice of performance metric, data filtering, and cross-validation setting. Such issues may seem like technical details, and they do not get much attention in published research. Nevertheless, their impact on experiments can be significant. We report experiments with six models for predicting problem-solving times in four introductory programming exercises. Our focus is not on these models per se but rather on the methodological choices necessary for performing these experiments. The results show, particularly, the importance of the choice of performance metric, including details of its computation and presentation.


title: Raising Teachers Empowerment in Gamification Design of Adaptive Learning Systems: A Qualitative Research

text: Despite the positive outcomes obtained through the application of gamification in the technology-enhanced learning context, previous studies have also reported unexpected results concerning students’ engagement, learning outcomes, and motivation in gamified learning systems. To increase the chances of obtaining positive results in this context, this article proposes a “gamification analytics model for teachers”. In this model, teachers are allowed to define interaction goals, monitor students’ interaction with the system’ learning resources and the gamification elements, and adapt the gamification design through missions to motivate disengaged students to achieve the interaction goals defined. However, the gamification analytics model-based design concepts that will be implemented to support the learning process should be well-planned to teachers’ needs. Hence, one of the contributions of this paper is the validation of twenty design concepts based on the gamification analytics model for teachers by using the speed dating method. Our results suggest that teachers judged useful/relevant visualize students’ interaction with gamification elements such as missions, levels to help them understand the students’ status, but did not evaluate the visualization of the interaction of students with trophies relevant. Teachers also highly evaluated the creation of personalized missions for a student or a specific group as relevant to help demotivated students to engage and achieve the desired goals. Therefore, this study provides some relevant insights to guide the design and re-design of gamified adaptive learning systems.


title: Effect of Non-mandatory Use of an Intelligent Tutoring System on Students’ Learning

text: Numerous controlled studies prove the effectiveness of Intelligent Tutoring Systems (ITSs). But what happens when ITSs are available to students for voluntary practice? EER-Tutor is a mature ITS which was previously found effective in controlled experiments. Students can use EER-Tutor for tutored problem solving, and there is also a special mode allowing students to develop solutions for the course assignment without receiving feedback. In this paper, we report the observations from two classes of university students using EER-Tutor. In 2018, the system was available for completely voluntary practice. We hypothesized that the students’ pre-existing knowledge and the time spent in EER-Tutor, mediated by the number of attempted EER-Tutor problems, contribute to the students’ scores on the assignment. All but one student used EER-Tutor to draw their assignment solutions, and 77% also used it for tutored problem solving. All our hypotheses were confirmed. Given the found benefits of tutored problem solving, we modified the assignment for the 2019 class so that the first part required students to solve three problems in EER-Tutor (without feedback), while the second part was similar to the 2018 assignment. Our hypothesized model fits the data well and shows the positive relationship between the three set problems on the overall system use, and the assignment scores. In 2019, 98% of the class engaged in tutored problem solving. The 2019 class also spent significantly more time in the ITS, solved significantly more problems and achieved higher scores on the assignment.


title: Improving Affect Detection in Game-Based Learning with Multimodal Data Fusion

text: Accurately recognizing learner affect is critically important for enabling affect-responsive learning environments to support student learning and engagement. Multimodal affect detection combining sensor-based and sensor-free approaches has shown significant promise in both laboratory and classroom settings. However, important questions remain regarding which data channels are most predictive and how they should be combined. In this paper, we investigate a multimodal affect detection framework that integrates motion tracking-based posture data and interaction-based trace data to recognize the affective states of students engaged with a game-based learning environment for emergency medical training. We compare several machine learning-based affective models using competing feature-level and decision-level multimodal data fusion approaches. Results indicate that multimodal affect detectors induced using joint feature representations from posture-based and interaction-based data channels yield improved accuracy relative to unimodal models across several learner-centered affective states. These findings point toward implications for the design of multimodal affect-responsive learning environments that support learning and engagement.


title: Fooling Automatic Short Answer Grading Systems

text: With the rising success of adversarial attacks on many NLP tasks, systems which actually operate in an adversarial scenario need to be reevaluated. For this purpose, we pose the following research question: How difficult is it to fool automatic short answer grading systems? In particular, we investigate the robustness of the state of the art automatic short answer grading system proposed by Sung et al. towards cheating in the form of universal adversarial trigger employment. These are short token sequences that can be prepended to students’ answers in an exam to artificially improve their automatically assigned grade. Such triggers are especially critical as they can easily be used by anyone once they are found. In our experiments, we discovered triggers which allow students to pass exams with passing thresholds of [Formula: see text] without answering a single question correctly. Furthermore, we show that such triggers generalize across models and datasets in this scenario, nullifying the defense strategy of keeping grading models or data secret.


title: A Data-Driven Student Model to Provide Adaptive Support During Video Watching Across MOOCs

text: MOOCs have great potential to innovate education, but lack of personalization. In this paper, we show how FUMA, a data-driven framework for student modeling and adaptation, can help understand how to provide personalized support to MOOCs students, specifically targeting video watching behaviors. We apply FUMA across several MOOCs to show how to: (i) discover video watching behaviors that can be detrimental for or conductive to learning; (ii) use these behaviors to detect ineffective learners at different weeks of MOOCs usage. We discuss how these behaviors can be used to define personalized support to effective MOOC video usage regardless of the target course.


title: Introducing a Framework to Assess Newly Created Questions with Natural Language Processing

text: Statistical models such as those derived from Item Response Theory (IRT) enable the assessment of students on a specific subject, which can be useful for several purposes (e.g., learning path customization, drop-out prediction). However, the questions have to be assessed as well and, although it is possible to estimate with IRT the characteristics of questions that have already been answered by several students, this technique cannot be used on newly generated questions. In this paper, we propose a framework to train and evaluate models for estimating the difficulty and discrimination of newly created Multiple Choice Questions by extracting meaningful features from the text of the question and of the possible choices. We implement one model using this framework and test it on a real-world dataset provided by CloudAcademy, showing that it outperforms previously proposed models, reducing by 6.7% the RMSE for difficulty estimation and by 10.8% the RMSE for discrimination estimation. We also present the results of an ablation study performed to support our features choice and to show the effects of different characteristics of the questions’ text on difficulty and discrimination.


title: Automated Analysis of Middle School Students’ Written Reflections During Game-Based Learning

text: Game-based learning environments enable students to engage in authentic, inquiry-based learning. Reflective thinking serves a critical role in inquiry-based learning by encouraging students to think critically about their knowledge and experiences in order to foster deeper learning processes. Free-response reflection prompts can be embedded in game-based learning environments to encourage students to engage in reflection and externalize their reflection processes, but automatically assessing student reflection presents significant challenges. In this paper, we present a framework for automatically assessing students’ written reflection responses during inquiry-based learning in Crystal Island, a game-based learning environment for middle school microbiology. Using data from a classroom study involving 153 middle school students, we compare the effectiveness of several computational representations of students’ natural language responses to reflection prompts—GloVe, ELMo, tf-idf, unigrams—across several machine learning-based regression techniques (i.e., random forest, support vector machine, multi-layer perceptron) to assess the depth of student reflection responses. Results demonstrate that assessment models based on ELMo deep contextualized word representations yield more accurate predictions of students’ written reflection depth than competing techniques. These findings point toward the potential of leveraging automated assessment of student reflection to inform real-time adaptive support for inquiry-based learning in game-based learning environments.


title: Far from Success – Far from Feedback Acceptance? The Influence of Game Performance on Young Students’ Willingness to Accept Critical Constructive Feedback During Play

text: In a learning situation, feedback is of great importance in order to help a student to correct a possible misconception. However, previous research shows that many students tend to avoid feedback regarding failures, including critical constructive feedback (CCF) that is intended to support and guide them. This is especially true for lower-achieving students, who might perceive feedback as an ego-threat, and therefore protect themselves by neglecting it. However, it has been shown that such neglect can be suppressed by using teachable agents (TA’s). Another, but less studied factor that influences feedback acceptance is the degree or extent of failure when trying to solve a task. The present study explores if and how momentary performance levels influence middle school students’ willingness to accept CCF when playing an educational game in history – with or without a TA. On the basis of teacher assessments of the students’ general skills, data logs and analyses of sequential patterns, we concluded that the willingness to accept CCF differs between students, but also between conditions and situations. One major finding is that a TA supports the students to more readily embrace CCF, even if the effect is larger for lower-achieving students. Another finding is that indications of being far from succeeding, such as low success rates or repeated trials and revisions, have a negative impact on feedback acceptance, even if a TA mitigates some of this influence. The implications of these results are discussed in relation to meta-cognitive aspects of learning and to educational software design.


title: Remember the Facts? Investigating Answer-Aware Neural Question Generation for Text Comprehension

text: Reading is a crucial skill in the 21st century. Thus, scaffolding text comprehension by automatically generated questions may greatly profit learners. Yet, the state-of-the-art methods for automatic question generation, answer-aware neural question generators (NQGs), are rarely seen in the educational domain. Hence, we investigate the quality of questions generated by a novel approach comprising an answer-aware NQG and two novel answer candidate selection strategies based on semantic graph matching. In median, the approach generates clear, answerable and useful factual questions outperforming an answer-unaware NQG on educational datasets as shown by automatic and human evaluation. Furthermore, we analyze the types of questions generated, showing that the question types differ across answer selection strategies yet remain factual.


title: Exploring the Impact of Simple Explanations and Agency on Batch Deep Reinforcement Learning Induced Pedagogical Policies

text: In recent years, Reinforcement learning (RL), especially Deep RL (DRL), has shown outstanding performance in video games from Atari, Mario, to StarCraft. However, little evidence has shown that DRL can be successfully applied to real-life human-centric tasks such as education or healthcare. Different from classic game-playing where the RL goal is to make an agent smart, in human-centric tasks the ultimate RL goal is to make the human-agent interactions productive and fruitful. Additionally, in many real-life human-centric tasks, data can be noisy and limited. As a sub-field of RL, batch RL is designed for handling situations where data is limited yet noisy, and building simulations is challenging. In two consecutive classroom studies, we investigated applying batch DRL to the task of pedagogical policy induction for an Intelligent Tutoring System (ITS), and empirically evaluated the effectiveness of induced pedagogical policies. In Fall 2018 (F18), the DRL policy is compared against an expert-designed baseline policy and in Spring 2019 (S19), we examined the impact of explaining the batch DRL-induced policy with student decisions and the expert baseline policy. Our results showed that 1) while no significant difference was found between the batch RL-induced policy and the expert policy in F18, the batch RL-induced policy with simple explanations significantly improved students’ learning performance more than the expert policy alone in S19; and 2) no significant differences were found between the student decision making and the expert policy. Overall, our results suggest that pairing simple explanations with induced RL policies can be an important and effective technique for applying RL to real-life human-centric tasks.


title: Use of Adaptive Feedback in an App for English Language Spontaneous Speech

text: Language learning apps have become increasingly popular. However, most of these apps target the first stages of learning a new language and are limited in the type of feedback that can be provided to users’ spontaneous spoken responses. The English Language Artificial Intelligence (ELAi) app was developed to address this gap by providing users with a variety of prompts for spontaneous speech and adaptive, targeted feedback based on the automatic evaluation of spoken responses. Feedback in the ELAi app was presented across multiple pages such that users could choose the amount and depth of feedback that they wanted to receive. The present work evaluates how 94 English language learners interacted with the app. We focused on participants’ use of the feedback pages and whether or not performance on spontaneous speech improved over the course of using the app. The findings revealed that users were most likely to access the most shallow feedback page, but use of the feedback pages differed based on the total number of sessions that users completed with the app. Users showed improvement in their response performance over the course of using the app, which suggests that the design of repeated practice and adaptive, targeted feedback in the ELAi app is promising. Patterns of feedback page use are discussed further as well as potential design modifications that could increase the use of feedback and maximize improvement in English language spontaneous speech.


title: To Tailor or Not to Tailor Gamification? An Analysis of the Impact of Tailored Game Elements on Learners’ Behaviours and Motivation

text: Gamification, defined as the use of game elements in non game situations, is a widely used method to foster learner engagement and motivation. It is generally accepted that in order to be effective, gamification should be tailored to users. Currently, most systems adapt by assigning different game elements based on a single learner profile (e.g. dominant player type, personality or gender). However, there is no study yet that analyse the effect of combining several profiles. In this paper, we study the usage data from 258 students who used a gamified learning environment as a part of their mathematics class. By simulating different adaptation techniques, we show that the learner model chosen to tailor gamification has significant effects on learners’ motivation and engaged behaviours depending on the profile(s) used in this context. We also show that tailoring to initial motivation to learn mathematics can improve intrinsic motivation. Finally, we show that tailoring to both player type and motivation profiles can improve intrinsic motivation, and decrease amotivation, compared to a single adaptation only based on learner motivation. We discuss the implications of our findings regarding the choice of a learner model for tailoring gamification in educational environments.


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title: AI Enabled Tutor for Accessible Training

text: A significant number of jobs require highly skilled labor which necessitate training on pre-requisite knowledge. Examples include jobs in military, technical field such computer science, large scale fulfillment centers such as Amazon. Moreover, making such jobs accessible to the disabled population requires even more pre-requisite training such as knowledge of sign language. An artificial intelligent (AI) agent can potentially act as a tutor for such pre-requisite training. This will not only reduce resource requirements for such training but also decrease the time taken for making personnel job ready. In this paper, we develop an AI tutor that can teach users gestures that are required on the field as a pre-requisite. The AI tutor uses a model learning technique that learns the gestures performed by experts. It then uses a model comparison technique to compare a learner with the expert gesture and provides feedback for the learner to improve.


title: Student Dropout Prediction

text: Among the many open problems in the learning process, students dropout is one of the most complicated and negative ones, both for the student and the institutions, and being able to predict it could help to alleviate its social and economic costs. To address this problem we developed a tool that, by exploiting machine learning techniques, allows to predict the dropout of a first-year undergraduate student. The proposed tool allows to estimate the risk of quitting an academic course, and it can be used either during the application phase or during the first year, since it selectively accounts for personal data, academic records from secondary school and also first year course credits. Our experiments have been performed by considering real data of students from eleven schools of a major University.


title: Using Thinkalouds to Understand Rule Learning and Cognitive Control Mechanisms Within an Intelligent Tutoring System

text: Cognitive control and rule learning are two important mechanisms that explain how goals influence behavior and how knowledge is acquired. These mechanisms are studied heavily in cognitive science literature within highly controlled tasks to understand human cognition. Although they are closely linked to the student behaviors that are often studied within intelligent tutoring systems (ITS), their direct effects on learning have not been explored. Understanding these underlying cognitive mechanisms of beneficial and harmful student behaviors can provide deeper insight into detecting such behaviors and improve predictive models of student learning. In this paper, we present a thinkaloud study where we asked students to narrate their thought processes while solving probability problems in ASSISTments. Students are randomly assigned to one of two conditions that are designed to induce the two modes of cognitive control based on the Dual Mechanisms of Control framework. We also observe how the students go through the phases of rule learning as defined in a rule learning paradigm. We discuss the effects of these different mechanisms on learning, and how the information they provide can be used in student modeling.


title: Comprehensive Views of Math Learners: A Case for Modeling and Supporting Non-math Factors in Adaptive Math Software

text: Adaptive math software supports students’ learning by targeting specific math knowledge components. However, widespread use of adaptive math software in classrooms has not led to the expected changes in student achievement, particularly for racially minoritized students and students situated in poverty. While research has shown the power of human mentors to support student learning and reduce opportunity gaps, mentoring support could be optimized by using educational technology to identify the specific non-math factors that are disrupting students’ learning and direct mentors to appropriate resources related to those factors. In this paper, we present an analysis of one non-math factor—reading comprehension—that has been shown to influence math learning. We predict math performance using this non-math factor and show that it contributes novel explanatory value in modeling students’ learning behaviors. Through this analysis, we argue that educational technology could better address the learning needs of the whole student by modeling non-math factors. We suggest future research should take this learning analytics approach to identify the many different kinds of motivational and non-math content challenges that arise when students are learning from adaptive math software. We envision analyses such as those presented in this paper enabling greater individualization within adaptive math software that takes into account not only math knowledge and progress but also non-math factors.


title: Exploring How Gender and Enjoyment Impact Learning in a Digital Learning Game

text: In digital learning games, do game mechanics that promote learning and those that promote enjoyment have different effects on students’ experience? Do males or females learn from or enjoy games more? We explored these questions in Decimal Point, a digital learning game that teaches decimal numbers and decimal operations to middle school students. In this work, we conducted a classroom study with two versions of the game, one that encourages students to play to learn and one that encourages students to play for enjoyment. We compared these two conditions to a control condition that is neutral regarding learning and enjoyment. Our results indicated that the enjoyment-focused group learned more efficiently than the control group, and that females had higher learning gains than males across all conditions, particularly on the near and middle transfer learning items. Post hoc analyses also revealed that the learning-focused group engaged in re-practicing the same mini-games, while the enjoyment-focused group demonstrated more exploration of different mini-games. These findings suggest that emphasizing learning or enjoyment can result in distinctive gameplay behaviors from students, and that our game can help bridge the typical gender gap in math education.


title: Transfer of Automated Performance Feedback Models to Different Specimens in Virtual Reality Temporal Bone Surgery

text: Virtual reality has gained popularity as an effective training platform in many fields including surgery. However, it has been shown that the availability of a simulator alone is not sufficient to promote practice. Therefore, simulator-based surgical curricula need to be developed and integrated into existing surgical training programs. As practice variation is an important aspect of a surgical curriculum, surgical simulators should support practice on multiple specimens. Furthermore, to ensure that surgical skills are acquired, and to support self-guided learning, automated feedback on performance needs to be provided during practice. Automated feedback is typically provided by comparing real-time performance with expert models generated from pre-collected data. Since collecting data on multiple specimens for the purpose of developing feedback models is costly and time-consuming, methods of transferring feedback from one specimen to another should be investigated. In this paper, we discuss a simple method of feedback transfer between specimens in virtual reality temporal bone surgery and validate the accuracy and effectiveness of the transfer through a user study.


title: Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching

text: This paper presents Moodoo, a system that models how teachers make use of classroom spaces by automatically analysing indoor positioning traces. We illustrate the potential of the system through an authentic study aimed at enabling the characterisation of teachers’ instructional behaviours in the classroom. Data were analysed from seven teachers delivering three distinct types of classes to +190 students in the context of physics education. Results show exemplars of how teaching positioning traces reflect the characteristics of the learning designs and can enable the differentiation of teaching strategies related to the use of classroom space. The contribution of the paper is a set of conceptual mappings from x − y positional data to meaningful constructs, grounded in the theory of Spatial Pedagogy, and its implementation as a composable library of open source algorithms. These are to our knowledge the first automated spatial metrics to map from low-level teacher’s positioning data to higher-order spatial constructs.


title: Strategies for Deploying Unreliable AI Graders in High-Transparency High-Stakes Exams

text: We describe the deployment of an imperfect NLP-based automatic short answer grading system on an exam in a large-enrollment introductory college course. We characterize this deployment as both high stakes (the questions were on an mid-term exam worth 10% of students’ final grade) and high transparency (the question was graded interactively during the computer-based exam and correct solutions were shown to students that could be compared to their answer). We study two techniques designed to mitigate the potential student dissatisfaction resulting from students incorrectly not granted credit by the imperfect AI grader. We find (1) that providing multiple attempts can eliminate first-attempt false negatives at the cost of additional false positives, and (2) that students not granted credit from the algorithm cannot reliably determine if their answer was mis-scored.


title: Recommending Insightful Drill-Downs Based on Learning Processes for Learning Analytics Dashboards

text: Learning Analytics Dashboards (LADs) make use of rich and complex data about students and their learning activities to assist educators in understanding and making informed decisions about student learning and the design and improvement of learning processes. With the increase in the volume, velocity, variety and veracity of data on students, manual navigation and sense-making of such multi-dimensional data have become challenging. This paper proposes an analytical approach to assist LAD users with navigating the large set of possible drill-down actions to identify insights about learning behaviours of the sub-cohorts. A distinctive feature of the proposed approach is that it takes a process mining lens to examine and compare students’ learning behaviours. The process oriented approach considers the flow and frequency of the sequences of performed learning activities, which is increasingly recognised as essential for understanding and optimising learning. We present results from an application of our approach in an existing LAD using a course with 875 students, with high demographic and educational diversity. We demonstrate the insights the approach enables, exploring how the learning behaviour of an identified sub-cohort differs from the remaining students and how the derived insights can be used by instructors.


title: Predicting Gaps in Usage in a Phone-Based Literacy Intervention System

text: Educational technologies may help support out-of-school learning in contexts where formal schooling fails to reach every child, but children may not persist in using such systems to learn at home. Prior research has developed methods for predicting learner dropout but primarily for adults in formal courses and Massive Open Online Courses (MOOCs), not for children’s voluntary ed tech usage. To support early literacy in rural contexts, our research group developed and deployed a phone-based literacy technology with rural families in Côte d’Ivoire in two longitudinal studies. In this paper, we investigate the feasibility of using time-series classification models trained on system log data to predict gaps in children’s voluntary usage of our system in both studies. We contribute insights around important features associated with sustained system usage, such as children’s patterns of use, performance on the platform, and involvement from other adults in their family. Finally, we contribute design implications for predicting and supporting learners’ voluntary, out-of-school usage of mobile learning applications in rural contexts.


title: MACER: A Modular Framework for Accelerated Compilation Error Repair

text: Automated compilation error repair, the problem of suggesting fixes to buggy programs that fail to compile, has pedagogical applications for novice programmers who find compiler error messages cryptic and unhelpful. Existing works frequently involve black-box application of generative models, e.g. sequence-to-sequence prediction (TRACER) or reinforcement learning (RLAssist). Although convenient, this approach is inefficient at targeting specific error types as well as increases training costs. We present MACER, a novel technique for accelerated error repair based on a modular segregation of the repair process into repair identification and repair application. MACER uses powerful yet inexpensive learning techniques such as multi-label classifiers and rankers to first identify the type of repair required and then apply the suggested repair. Experiments indicate that this fine-grained approach offers not only superior error correction, but also much faster training and prediction. On a benchmark dataset of 4K buggy programs collected from actual student submissions, MACER outperforms existing methods by 20% at suggesting fixes for popular errors while being competitive or better at other errors. MACER offers a training time speedup of [Formula: see text] over TRACER and [Formula: see text] over RLAssist, and a test time speedup of [Formula: see text] over both.


title: Computational approaches in viral ecology

text: Dynamic virus-host interactions play a critical role in regulating microbial community structure and function. Yet for decades prior to the genomics era, viruses were largely overlooked in microbial ecology research, as only low-throughput culture-based methods of discovering viruses were available. With the advent of metagenomics, culture-independent techniques have provided exciting opportunities to discover and study new viruses. Here, we review recently developed computational methods for identifying viral sequences, exploring viral diversity in environmental samples, and predicting hosts from metagenomic sequence data. Methods to analyze viruses in silico utilize unconventional approaches to tackle challenges unique to viruses, such as vast diversity, mosaic viral genomes, and the lack of universal marker genes. As the field of viral ecology expands exponentially, computational advances have become increasingly important to gain insight into the role viruses in diverse habitats.


title: Outcomes of Bariatric Surgery in African Americans: an Analysis of the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) Data Registry

text: BACKGROUND: The incidence of obesity is disproportionally high in African Americans (AA) in the United States. This study compared outcomes for AA patients undergoing Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) with non-AA patients. METHODS: The MBSAQIP database was reviewed for RYGB and SG patients (2015–2017). Patients were identified as AA or non-AA and grouped to RYGB or SG. Combined and univariate analyses were performed on unmatched/propensity matched populations to assess outcomes. RESULTS: After applying exclusion criteria, 75,409 AA and 354,305 non-AA patients remained. Univariate analysis identified AA-RYGB and AA-SG patients were heavier and younger than non-AA patients. Overall, AA patients tended to have fewer preoperative comorbidities than non-AA patients with the majority of AA comorbidities related to hypertension and renal disease. Analysis of propensity matched data confirmed AA bariatric surgery patients had increased cardiovascular-related disease incidence compared with non-AA patients. Perioperatively, AA-RYGB patients had longer operative times, increased rates of major complications/ICU admission, and increased incidence of 30-day readmission, re-intervention, and reoperation, concomitant with lower rates of minor complications/superficial surgical site infection (SSI) compared with non-AA patients. For SG, AA patients had longer operative times and higher rates of major complications and 30-day readmission, re-intervention, and mortality, coupled with fewer minor complications, superficial/organ space SSI, and leak. CONCLUSION: African American patients undergoing bariatric surgery are younger and heavier than non-AA patients and present with different comorbidity profiles. Overall, AAs exhibit worse outcomes following RYGB or SG than non-AA patients, including increased mortality rates in AA-SG patients.


title: “Ik weet niet wat lock-down betekent”: Moeite met lezen en schrijven in tijden van corona

text: Meer dan twee miljoen Nederlanders hebben moeite met lezen en schrijven. Zij hebben stress omdat ze informatie niet altijd begrijpen. Zeker nu, in corona-tijd. Huisartsen vinden het moeilijk om begrijpelijke uitleg te geven. Daarom hebben we dit artikel geschreven. Zodat huisartsen zien hoe ze over corona kunnen praten met makkelijke woorden. Huisartsen kunnen de terugvraag-methode gebruiken. Zo weten ze of de patiënt de informatie begrepen heeft. Het artikel zelf schreven we in makkelijke taal. Met korte zinnen, zonder moeilijke woorden en met koppel-tekens. Als voorbeeld dat het goed kan om makkelijke woorden te gebruiken.


Wo bleibt Ihre Praxis?


title: Freshwater phytoplankton diversity: models, drivers and implications for ecosystem properties

text: Our understanding on phytoplankton diversity has largely been progressing since the publication of Hutchinson on the paradox of the plankton. In this paper, we summarise some major steps in phytoplankton ecology in the context of mechanisms underlying phytoplankton diversity. Here, we provide a framework for phytoplankton community assembly and an overview of measures on taxonomic and functional diversity. We show how ecological theories on species competition together with modelling approaches and laboratory experiments helped understand species coexistence and maintenance of diversity in phytoplankton. The non-equilibrium nature of phytoplankton and the role of disturbances in shaping diversity are also discussed. Furthermore, we discuss the role of water body size, productivity of habitats and temperature on phytoplankton species richness, and how diversity may affect the functioning of lake ecosystems. At last, we give an insight into molecular tools that have emerged in the last decades and argue how it has broadened our perspective on microbial diversity. Besides historical backgrounds, some critical comments have also been made. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10750-020-04332-9) contains supplementary material, which is available to authorized users.


title: Dialogue in community: conditions and enablers for teacher professional development in Catholic schools

text: This article explores conditions and enablers for teacher professional learning in religious education in Catholic schools. It examines the impact on religion of the pluralising and secularising influences in Australia today and identifies what this asks of religious education and religious educators in response. New pedagogical approaches which emphasise identity formation as critical to religious education, make teacher identity formation and ongoing learning vital for Catholic schools to undertake. The article proposes that teacher professional learning in Catholic schools focus on developing the following three dispositions and skills: self-awareness and reflexivity; openness to faith perspectives; and courageous communication skills. The article investigates teacher professional learning theory and research to make recommendations for teacher development in religious education. It raises two concepts as integral to professional learning in a Catholic context: Dialogue and Community. These concepts are highlighted as theologically and strategically important for Catholic school communities to attend to and explore.


title: Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach

text: Bitcoin is a decentralized cryptocurrency, which is a type of digital asset that provides the basis for peer-to-peer financial transactions based on blockchain technology. One of the main problems with decentralized cryptocurrencies is price volatility, which indicates the need for studying the underlying price model. Moreover, Bitcoin prices exhibit non-stationary behavior, where the statistical distribution of data changes over time. This paper demonstrates high-performance machine learning-based classification and regression models for predicting Bitcoin price movements and prices in short and medium terms. In previous works, machine learning-based classification has been studied for an only one-day time frame, while this work goes beyond that by using machine learning-based models for one, seven, thirty and ninety days. The developed models are feasible and have high performance, with the classification models scoring up to 65% accuracy for next-day forecast and scoring from 62 to 64% accuracy for seventh–ninetieth-day forecast. For daily price forecast, the error percentage is as low as 1.44%, while it varies from 2.88 to 4.10% for horizons of seven to ninety days. These results indicate that the presented models outperform the existing models in the literature.


title: Nonlinear dynamics of a time-delayed epidemic model with two explicit aware classes, saturated incidences, and treatment

text: Whenever a disease emerges, awareness in susceptibles prompts them to take preventive measures, which influence individuals’ behaviors. Therefore, we present and analyze a time-delayed epidemic model in which class of susceptible individuals is divided into three subclasses: unaware susceptibles, fully aware susceptibles, and partially aware susceptibles to the disease, respectively, which emphasizes to consider three explicit incidences. The saturated type of incidence rates and treatment rate of infectives are deliberated herein. The mathematical analysis shows that the model has two equilibria: disease-free and endemic. We derive the basic reproduction number [Formula: see text] of the model and study the stability behavior of the model at both disease-free and endemic equilibria. Through analysis, it is demonstrated that the disease-free equilibrium is locally asymptotically stable when [Formula: see text] , unstable when [Formula: see text] , and linearly neutrally stable when [Formula: see text] for the time delay [Formula: see text] . Further, an undelayed epidemic model is studied when [Formula: see text] , which reveals that the model exhibits forward and backward bifurcations under specific conditions, which also has important implications in the study of disease transmission dynamics. Moreover, we investigate the stability behavior of the endemic equilibrium and show that Hopf bifurcation occurs near endemic equilibrium when we choose time delay as a bifurcation parameter. Lastly, numerical simulations are performed in support of our analytical results.


The rapidly evolving treatment landscape of advanced prostate, bladder, and renal cell carcinomas


title: Understanding Rapport over Multiple Sessions with a Social, Teachable Robot

text: Social robots have been shown to be effective educational tools. Rapport, or interpersonal closeness, can lead to better human-robot interactions and positive learning outcomes. Prior research has investigated the effects of social robots on student rapport and learning in a single session, but little is known about how individuals build rapport with a robot over multiple sessions. We reported on a case study in which 7 middle school students explained mathematics concepts to an intelligent teachable robot named Emma for five sessions. We modeled learners’ rapport-building linguistic strategies to understand whether the ways middle school students build rapport with the robot over time follow the same trends as human conversation, and how individual differences might mediate the rapport between human and robot.


title: Bridging Over from Learning Videos to Learning Resources Through Automatic Keyword Extraction

text: The presented system and approach facilitate intelligent, contextualized information access for learners based on automatic learning video analysis. The underlying workflow starts with automatically extracting keywords from learning videos followed by the generation of recommendations of learning materials. The approach has been implemented and investigated in a user study in a real-world VET setting. The study investigated the acceptance, perceived quality and relevance of automatically extracted keywords and automatically generated learning resource recommendations in the context of a set of learning videos related to chemistry and chemical engineering. The results indicate that such extracted keywords are in line with user-generated keywords and summarize the content of videos quite well. Also, they can be used as search key to find relevant learning resources.


title: When Lying, Hiding and Deceiving Promotes Learning - A Case for Augmented Intelligence with Augmented Ethics

text: If AI systems are to be used for truly human decision-making in education, teachers will need better support for deciding upon educational interventions and strategies on an ethically informed basis. As indicated by a recent call by the AIED Society to focus on the FATE (Fairness, Accountability, Transparency, and Ethics) of AI in education, fundamental issues in this area remain to be explicated, and teachers’ perspectives need to be accounted for. The paper offers examples of how AI may serve to promote learning but at the cost of presenting limited or untruthful information to the student. For example, false information about a student’s current progress may motivate students to finish a task they would otherwise give up; hiding information from the student that is disclosed to the teacher may decrease students’ cognitive load while supporting the teacher’s strategic choices, and deceiving the student as to the actual nature of the task or interaction, such as when using virtual agents, can increase students’ efforts towards learning. Potential conflicts between such scenarios and basic values of FATE are discussed, and the basis for developing an “augmented ethics” system to support teachers’ decision-making is presented.


title: A General Multi-method Approach to Design-Loop Adaptivity in Intelligent Tutoring Systems

text: Design-loop adaptivity, which involves data-driven redesign of an instructional system based on student learning data, has shown promise in improving student learning. We present a general, systematic approach that combines new and existing data mining and instructional design methods to redesign intelligent tutors. Our approach is driven by the main goal of identifying knowledge components that are demonstrably difficult for students to learn and to optimize effective and efficient practice of them. We applied this approach to redesigning an algebraic symbolization tutor. Our classroom study with 76 high school freshmen shows that, compared to the original tutor, the redesigned tutor led to higher learning efficiency on more difficult skills, higher learning gain on unscaffolded whole tasks, and more robust transfer to less practiced tasks. Our work provides general guidance for performing design-loop adaptations for continuous improvement of intelligent tutors.


title: A Framework for Exploring the Impact of Tutor Practices on Learner Self-regulation in Online Environments

text: There is increasing interest in the conceptualization of Self-Regulated learning (SRL) as a dynamic process which unfolds over the course of a learning activity. This is partly because this conceptualization could potentially be operationalized and used as the basis for AI and analytics tools which monitor and scaffold SRL in real-time. However, while there is an abundance of research on theories of SRL, little research explicitly reviews and operationalizes such theoretical considerations. Work is needed to develop frameworks for the practical applications of fundamental SRL theories, helping researchers move from conceptual considerations to operationalization in real world settings. In this paper, we propose a theoretically grounded framework for investigating SRL in the context of online tutoring for upper primary school learners. SRL is interpreted as a social learning construct, and the framework proposed is designed to investigate the influence of tutor practices on the development of learners’ SRL. We present the results of a pilot study that explored the applicability of the framework.


title: Multi-document Cohesion Network Analysis: Visualizing Intratextual and Intertextual Links

text: Reading comprehension requires readers to connect ideas within and across texts to produce a coherent mental representation. One important factor in that complex process regards the cohesion of the document(s). Here, we tackle the challenge of providing researchers and practitioners with a tool to visualize text cohesion both within (intra) and between (inter) texts. This tool, Multi-document Cohesion Network Analysis (MD-CNA), expands the structure of a CNA graph with lexical overlap links of multiple types, together with coreference links to highlight dependencies between text fragments of different granularities. We introduce two visualizations of the CNA graph that support the visual exploration of intratextual and intertextual links. First, a hierarchical view displays a tree-structure of discourse as a visual illustration of CNA links within a document. Second, a grid view available at paragraph or sentence levels displays links both within and between documents, thus ensuring ease of visualization for links spanning across multiple documents. Two use cases are provided to evaluate key functionalities and insights for each type of visualization.


title: EdNet: A Large-Scale Hierarchical Dataset in Education

text: Advances in Artificial Intelligence in Education (AIEd) and the ever-growing scale of Interactive Educational Systems (IESs) have led to the rise of data-driven approaches for knowledge tracing and learning path recommendation. Unfortunately, collecting student interaction data is challenging and costly. As a result, there is no public large-scale benchmark dataset reflecting the wide variety of student behaviors observed in modern IESs. Although several datasets, such as ASSISTments, Junyi Academy, Synthetic and STATICS are publicly available and widely used, they are not large enough to leverage the full potential of state-of-the-art data-driven models. Furthermore, the recorded behavior is limited to question-solving activities. To this end, we introduce EdNet, a large-scale hierarchical dataset of diverse student activities collected by Santa, a multi-platform self-study solution equipped with an artificial intelligence tutoring system. EdNet contains 131,417,236 interactions from 784,309 students collected over more than 2 years, making it the largest public IES dataset released to date. Unlike existing datasets, EdNet records a wide variety of student actions ranging from question-solving to lecture consumption to item purchasing. Also, EdNet has a hierarchical structure which divides the student actions into 4 different levels of abstractions. The features of EdNet are domain-agnostic, allowing EdNet to be easily extended to different domains. The dataset is publicly released for research purposes. We plan to host challenges in multiple AIEd tasks with EdNet to provide a common ground for the fair comparison between different state-of-the-art models and to encourage the development of practical and effective methods.


title: A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM

text: We present Korbit, a large-scale, open-domain, mixed-interface, dialogue-based intelligent tutoring system (ITS). Korbit uses machine learning, natural language processing and reinforcement learning to provide interactive, personalized learning online. Korbit has been designed to easily scale to thousands of subjects, by automating, standardizing and simplifying the content creation process. Unlike other ITS, a teacher can develop new learning modules for Korbit in a matter of hours. To facilitate learning across a wide range of STEM subjects, Korbit uses a mixed-interface, which includes videos, interactive dialogue-based exercises, question-answering, conceptual diagrams, mathematical exercises and gamification elements. Korbit has been built to scale to millions of students, by utilizing a state-of-the-art cloud-based micro-service architecture. Korbit launched its first course in 2019 and has over 7, 000 students have enrolled. Although Korbit was designed to be open-domain and highly scalable, A/B testing experiments with real-world students demonstrate that both student learning outcomes and student motivation are substantially improved compared to typical online courses.


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title: The Exploration of Feeling of Difficulty Using Eye-Tracking and Skin Conductance Response

text: Metacognitive experience (ME) plays an important role in self-regulated learning. To date, through mainly self-reporting methodology, metacognition assessment lacks objective evidence and therefore hinder the discussion of its subjective and implicit nature. In exploring ME, eye-tracking and skin conductance response (SCR) offer certain advantages over self-reporting methods. However, to date, most studies tend to focus on utilizing these measures to explore metacognitive skills (MS) rather than ME. Also, while some studies do explore ME with these measures tend to utilize the data from a summative perspective rather than aligning the data with the real-time ME behaviours. Based on previous works in this field, this research will discuss how feeling of difficulty (i.e. a type of ME) functions in real-time based on the hypothesis that eye-tracking and SCR data can provide objective measures. Through such, a better understanding of how FOD functions could be gained and therefore contribute to the support of learners’ metacognitive competencies.


title: Deep Knowledge Tracing with Transformers

text: In this work, we propose a Transformer-based model to trace students’ knowledge acquisition. We modified the Transformer structure to utilize 1) the association between questions and skills and 2) the elapsed time between question steps. The use of question-skill associations allows the model to learn specific representation for frequently encountered questions while representing rare questions with their underline skill representations. The inclusion of elapsed time opens the opportunity to address forgetting. Our approach outperforms the state-of-the-art methods in the literature by roughly 10% in AUC with frequently used public datasets.


title: Modelling Learners in Crowdsourcing Educational Systems

text: Traditionally, learner models estimate a student’s knowledge state solely based on their performance on attempting assessment items. This can be attributed to the fact that in many traditional educational systems, students are primarily involved in just answering assessment items. In recent years, the use of crowdsourcing to support learning at scale has received significant attention. In crowdsourcing educational systems, in addition to attempting assessment items, students are engaged with other various tasks such as creating resources, creating solutions, rating the quality of resources, and giving feedback. Past studies have demonstrated that engaging students in meaningful crowdsourcing tasks, also referred to as learningsourcing, has pedagogical benefits that can enhance student learning. In this paper, we present a learner model that leverages data from students’ learnersourcing contributions alongside attempting assessment items towards modelling of students’ knowledge state. Results from an empirical study suggest that indeed crowdsourced contributions from students can effectively be used in modelling learners.


title: An Evaluation of Data-Driven Programming Hints in a Classroom Setting

text: Data-driven programming hints are a scalable way to support students when they are stuck by automatically offering suggestions and identifying errors. However, few classroom studies have investigated data-driven hints’ impact on students’ performance and learning. In this work, we ran a controlled experiment with 241 students in an authentic classroom setting, comparing students who learned with and without hints. We found no evidence that hints improved student performance or learning overall, and we discuss possible reasons why.


title: Exploring the Role of Perspective Taking in Educational Child-Robot Interaction

text: Perspective taking is an important skill to have and learn, which can be applied in many different domains and disciplines. While the ability to recognize other’s perspective develops in humans from childhood and solidifies during school years, it needs to be developed in robotic and artificial agents’ cognitive framework. In our quest to develop a cognitive model of perspective taking for agents and robots in educational contexts, we designed a task that requires the players (e.g., child and robot) to take the perspective of another, in order to complete and win the task successfully. In a preliminary study to test the system, we were able to evaluate children’s performance over four different age groups by focusing on their performance during the interaction with the robot. By analyzing children’s performance, we were able to make some assumptions about children’s understanding of the game and select the appropriate age group to participate in the main study.


title: Automated Prediction of Novice Programmer Performance Using Programming Trajectories

text: Online programming courses have become widely available and host thousands of learners every year. In these courses, participants must solve programming exercises by submitting partial solutions and checking the outcome. The sequence of partial solutions submitted by a student constitutes the programming trajectory followed by the student. In our work, we define a supervised machine learning algorithm that takes as input these programming trajectories and predicts whether a student will successfully complete the next exercise. We have validated our model with two different datasets: the first one is a set of problems from the online learning platform Robomission with over one hundred thousand exercises submitted. The second one comprises one hundred thousand exercises submitted to the Hour of Code challenge. The results obtained indicate that our model can accurately predict the future performance of the students. This work provides not only a new method to represent students’ programming trajectories but also an efficient approach to predict the students’ future performance. Furthermore, the information provided by the model can be used to select the students that would benefit from an intervention.


title: Helping Teachers Assist Their Students in Gamified Adaptive Educational Systems: Towards a Gamification Analytics Tool

text: In this paper, we present the results of a case study conducted to validate the effectiveness of our gamification analytics model for teachers proposed in [20]. To conduct this case study, we developed a tool to monitor and adapt gamification designs in gamified adaptive educational systems. Employing this tool, the case study was conducted in a real situation, and the findings suggest that the use of our model and tool improves students’ engagement, learning outcomes, and motivation.


title: Sequence-to-Sequence Models for Automated Text Simplification

text: A key writing skill is the capability to clearly convey desired meaning using available linguistic knowledge. Consequently, writers must select from a large array of idioms, vocabulary terms that are semantically equivalent, and discourse features that simultaneously reflect content and allow readers to grasp meaning. In many cases, a simplified version of a text is needed to ensure comprehension on the part of a targeted audience (e.g., second language learners). To address this need, we propose an automated method to simplify texts based on paraphrasing. Specifically, we explore the potential for a deep learning model, previously used for machine translation, to learn a simplified version of the English language within the context of short phrases. The best model, based on an Universal Transformer architecture, achieved a BLEU score of 66.01. We also evaluated this model’s capability to perform similar transformation to texts that were simplified by human experts at different levels.


title: Machine Learning and Student Performance in Teams

text: This project applies a variety of machine learning algorithms to the interactions of first year college students using the GroupMe messaging platform to collaborate online on a team project. The project assesses the efficacy of these techniques in predicting existing measures of team member performance, generated by self- and peer assessment through the Comprehensive Assessment of Team Member Effectiveness (CATME) tool. We employed a wide range of machine learning classifiers (SVM, KNN, Random Forests, Logistic Regression, Bernoulli Naive Bayes) and a range of features (generated by a socio-linguistic text analysis program, Doc2Vec, and TF-IDF) to predict individual team member performance. Our results suggest machine learning models hold out the possibility of providing accurate, real-time information about team and team member behaviors that instructors can use to support students engaged in team-based work, though challenges remain.


title: Interactive Pedagogical Agents for Learning Sequence Diagrams

text: Students struggle to learn sequence diagrams (SDs), as the designs must meet the requirements without violating the constraints imposed by other UML diagrams. Providing manual timely feedback, though effective, cannot scale for large classes. Our pedagogical agent combining data dependencies and quality metrics with rule-based techniques capturing consistency constraints allowed generation of immediate and holistic feedback. The scaffolding approach helped to lower the cognitive overload. The pre- and post-tests and survey results revealed substantially improved learning outcomes and student satisfaction.