Reviewerly/exHarmony huggingface dataset:
Modeling Student Discourse in Online Discussion Forums Using Semantic Similarity Based Topic Chains

Students' conversations in academic settings evolve over time and can be affected by events such as the COVID-19 pandemic. In this paper, we employ a Contextualized Topic Modeling technique to detect coherent topics from students' posts in online discussion forums. We construct topic chains by connecting semantically similar topics across months using Word Mover's Distance. Consistent academic discourse and contemporary events such as the COVID-19 outbreak and the Black Lives Matter movement were found among prominent topics. In later months, new themes around students' lived experiences emerged and evolved into discussions reflecting the shift in educational experiences. Results revealed a significant increase in more general topics after the onset of pandemic. Our proposed framework can also be applied to other contexts investigating temporal topic trends in large-scale text data.
Developing an Inclusive Q&amp;A Chatbot in Massive Open Online Courses

This study aims to examine massive open online course (MOOC) students' experiences with a natural language processing-based Q&A chatbot. Following the definition of 'inclusive learning' in MOOCs from the Universal Design of Learning approach, this study firstly compares students' behavioral intentions before and after using the chatbot. Next, this study investigates students' levels of several other learning experience domains after using the chatbot—teaching presence, cognitive presence, social presence, enjoyment, perceived use of ease, etc. After examining students' possible disparate learning experiences in these domains, this study investigates how age, gender, region, and native language factors influence students' learning experiences with the chatbot. Lastly, but most importantly, this study explores how demographic factors influence students' perception of chatbot interactions. If any are found, this study will focus on possible negative demographic factors that affect only certain groups of students to further examine how to improve a Q&A chatbot for inclusive learning in MOOCs.
Foreword to Machine Didactics: On Peer Learning of Artificial and Human Pupils

Process of human learning has many features in common with the process of machine learning. This allows for creation of human-AI tandems or smaller groups where all members of the tandem or a group learn and develop. Consistently with Vygotskyan and Piagetian theories of learning and role which peers and intersubjective relations play in such theories, we hypothesize that curricula can be established whereby human and artificial learners collaboratively learn together, resulting in a win-win situation for both organic and anorganic agents involved.
K-12BERT: BERT for K-12 Education

Online education platforms are powered by various NLP pipelines, which utilize models like BERT to aid in content curation. Since the inception of the pre-trained language models like BERT, there have also been many efforts toward adapting these pre-trained models to specific domains. However, there has not been a model specifically adapted for the education domain (particularly K-12) across subjects to the best of our knowledge. In this work, we propose to train a language model on a corpus of data curated by us across multiple subjects from various sources for K-12 education. We also evaluate our model, K-12BERT, on downstream tasks like hierarchical taxonomy tagging.
Detecting Teachers’ in-Classroom Interactions Using a Deep Learning Based Action Recognition Model

In-classroom observations often rely on developed protocols and human observers. However, it requires a lot of human effort. This study investigates how accurately the pre-trained action recognition model can label teacher's behaviors in the classroom. We adopt SlowFast, a state of the art action recognition model, to a real classroom at a junior-high school mathematics class in Japan. In a pilot study of a mathematics class in a junior high school, the pre-trained model had 92.7% accuracy to identify teacher's posture, 31.7% related to the teacher's interaction with objects, and 26.8% related to teacher-student interaction. Compared to the existing baseline (34.3%), our results indicate that the pre-trained model adopts well to classroom videos as well. Possible reasons for the low accuracy of the verbs in the last two categories are (1) the pre-trained model could not sufficiently deal with objects unique to the classroom, such as a whiteboard, and (2) the teacher wore masks as an infection control measure, which made it difficult to recognize teacher's talking behavior. This study provides an initial automated approach to have a teacher's in-classroom interaction dataset extracted from the class videos. One needs to be aware of the ethical implementation and then such deep learning technologies have potential for a data-driven paradigm for the teacher's in action reflection.
Interpretable Knowledge Gain Prediction for Vocational Preparatory E-Learnings

Vocational further education typically builds upon prior knowledge. For learners who lack this prior knowledge, preparatory e-learnings may help. Therefore, we wish to identify students who would profit from such an e-learning. We consider the example of a math e-learning for the Bachelor Professional of Chemical Production and Management (CCI). To estimate whether the e-learning would help, we employ a predictive model. Developing such a model in a real-world scenario confronted us with a range of challenges, such as small sample sizes, overfitting, or implausible model parameters. We describe how we addressed these challenges such that other practitioners can learn from our case study of employing data mining in vocational training.
Mobile Learning for Emergency Situations: Four Design Cases from Latin America

Abstract This practitioner-focused chapter addresses mobile learning in the Latin American context during the COVID-19 emergency. To guarantee continuity of education during the COVID-19 pandemic, instructors adopted remote education. Even though much of the remote education relied heavily on computers, millions of learners in Latin America do not have a household computer. Nonetheless, mobile connectivity is very high in Latin America, and therefore, mobile learning has greatly supported institutions during remote education. Mobile learning significantly supports learning at a distance in countries that face infrastructure challenges. Even more in the Latin American context, where mobile devices may be low-cost alternatives to computers. We present four design cases about mobile learning for continuity of education during emergencies. Each design case addresses a different country, audience, and content. The design cases focus on generic technology applications regularly used by practitioners and students. The four design cases are: (1) foreign language learning and social studies to 1st – fourth graders using online blogs in Brazil; (2) teaching STEM to 8th–12th graders through social media (i.e., YouTube/WhatsApp) in Panama; (3) education to 6th–12th graders through social media (i.e., YouTube/WhatsApp/Facebook) in Mexico, and (4) humanities higher education using instant messaging and cloud-based platforms (i.e., WhatsApp, Google Drive platform) in Colombia. Recommendations for practitioners and policymakers are provided.
RIVALRY IN SCIENCE AND CULTURAL AND HUMANISTIC VALUES

The nature of rivalry in the context of scientific development is comprehensively studied. Modern science, the research community, the field of commercial application of scientific achievements – a highly competitive environment. At the same time, science must remain a fundamental support for humanity for the further progress of mankind. Thus, excessive pragmatism and utilitarianism in science and technology must be constrained by cultural factors. In this regard, the introduction of the values of humanism and human-centeredness into the scientific sphere is becoming extremely important. It is emphasized that rivalry in science on the one hand contributes to the emergence of new discoveries, maintaining a competitive spirit in achieving the best results, and on the other hand – makes the scientific process more pragmatic and commercialized. In carrying out cognitive activities, the subjects of science are fighting not only for the best result as such, but also for mastering the truth as the most adequate system of knowledge.Emphasis is placed on the importance of humanistic motivating factors for the development of science, which will contribute to the establishment of creativity and non-involvement in scientific activities. To reproduce these factors, the general public atmosphere in relation to research activities is important – the assertion in public opinion, public policy of significance and respect for the work of the scientist. From the humanistic point of view, the activities of scientists should be aimed not at creating new weapons, means of destroying the natural environment, but at improving the adaptive capabilities of man, creating conditions for his harmonious existence with expanded opportunities in various dimensions: social, biological, technological, information and communication.
Assessment Results on the Blockchain: A Conceptual Framework

Assessments evaluate students' understanding of taught concepts. While students may be assessed on various concepts and in different learning environments, learning institutions often fail to provide students with a way to connect their learning and assessment results beyond a single institution. This results in problems such as the inability to understand one's readiness for a new program, the inability to decide the most suitable program to enrol after current learning, and difficulty finding the right institution to enrol based on one's profile. This work proposes a conceptual framework for connecting assessment results on the blockchain beyond a single institution in a secure, privacy-preserving, tamper-proof, and trusted manner. We also discuss some typical use cases, results of the evaluation and visualizations when students graduate from high school and enter higher institution.
Cognitive Diagnosis Focusing on Knowledge Components

Cognitive diagnosis is a crucial task in the field of educational measurement and psychology, which aims to diagnose the strengths and weaknesses of participants. Existing cognitive diagnosis methods only consider which of knowledge concepts are involved in the knowledge components of exercises, but ignore the problem that different knowledge concepts have different effects on practice scores in actual learning situations. This paper proposes the CDMFKC model, which considers the effect of different knowledge concepts on exercise scores, and uses neural networks to model complex interactions between students and exercise factors. We validate our model on three real datasets.
Using Open Source Technologies and Generalizable Procedures in Conversational and Affective Intelligent Tutoring Systems

In the last years, the educational field has been influenced by technological advances. The digital transformation in educational environments allows the incorporation of virtual teaching-learning environments, which allow or facilitate learning opportunities for students, showing, for example, where they make mistakes and providing personalized help whenever they require it. In addition, these systems provide permanent access availability whenever it is possible to access the Internet. Traditionally, simultaneously many students learn word problem-solving skills in the classroom through instruction from only one educational professional. The Intelligent Tutoring System (ITS) Hypergraph Based Problem Solver (HBPS) is capable of tutoring the whole process of solving arithmetic-algebraic word problems, in a personalized way and without imposing any restrictions on the resolution path. Nevertheless, the student-system interaction is performed through a traditional interface by selecting items from a drop-down menu and clicking on buttons. Since dialogue is the fundamental communication mechanism for human-human, we propose use a framework to improve the interaction of the HBPS using a conversational user interface that allows performing the same actions more easily using natural language as the main means of interaction. My thesis research focuses on two main topics. The first one is related to the incorporation of a conversational agent using an open source machine learning framework that is fully configurable. The second one in concerned with testing and modifying different neural architectures to improve performance in intent classification and entity extraction, in such a way that it can be exported to other mathematical domains.
Crime Detection and Analysis from Social Media Messages Using Machine Learning and Natural Language Processing Technique

Social media has dramatically influenced and changed the rate and the nature of crime in our society. The perpetrators cut across different age groups, social standing, and beliefs. The ability to be anonymous on social media and the lack of adequate resources to fight cybercrime are catalysts for the rise in criminal activities, especially in South Africa. We proposed a system that will analyse and detect crime in social media posts or messages. The new system can detect attacks and drug-related crime messages, hate speech, and offensive messages. Natural language processing algorithms were used for text tokenisation, stemming, and lemmatisation. Machine learning models such as support vector machines and random forest classifiers were used to classify texts. Using the support vector machine to detect crime in texts, we achieved 86% accuracy and using the random forest for crime analysis, 72% accuracy was achieved.
Authoring Inner Loops of Intelligent Tutoring Systems Using Collective Intelligence

The inner loops on the Intelligent Tutoring Systems (ITS) are responsible for analyzing step-by-step resolutions and providing the necessary support to students successfully complete a task. Building adequate inner loops that address students' struggles requires a considerable amount of time and several interactions with experts of the domain knowledge which increases the costs and the complexity to develop ITS. To address this problem, we proposed a novel approach to build inner loops using students' collective intelligence (CI), which every interaction of a student to solve an exercise contributes to the process of authoring the ITS domain model. To evaluate our approach, we developed an ITS in a domain of numerical expressions that was used by 147 students. As a result, we observed that our approach helps to create an ITS with comprehensive and adequate inner loops using less time when compared to the time reported in the literature and with little intervention from experts. To the best of our knowledge, this is the first domain-independent approach that uses CI in the process of authoring inner loops of ITS.
Assessing Readability of Learning Materials on Artificial Intelligence in English for Second Language Learners

Many scientific publications and materials on artificial intelligence (AI) have been written in English; however, for many AI learners, English is their second language. Therefore, the difficulty (readability) of online self-teaching texts on AI for English-as-a-second-language (ESL) learners is essential for determining the language support ESL learners need to learn AI. However, only a few studies have addressed this issue. Therefore, we identified the difficulty level of English self-teaching texts for ESL AI learners. Because large-scale testing for ESL learners is impractical owing to the financial costs and time involved, we built two distinctive automatic readability assessors: one using sophisticated deep-learning-based natural language processing (NLP) technology, and another using classic NLP based on word frequency and applied linguistics. We conducted our evaluation using AI research papers and university-level online course texts. Interestingly, the distinctive automatic assessors, which were trained on different datasets, showed similar results. Intermediate-level ESL learners could read approximately 10% of online course texts. We also showed that they are significantly easier to read than AI research papers for ESL learners, demonstrating their usefulness in AI learning.
Nonverbal Collaboration on Perceptual Learning Activities with Chemistry Visualizations

Classroom orchestration tools rely on research showing when individual or collaborative activities are effective. No prior research has investigated effects of collaboration on perceptual learning, which is nonverbal and inductive. An experiment compared individual learning to nonverbal, gesture-based collaboration on a perceptual training. Results show an advantage of gesture-based collaboration on pre-to-posttest gains, especially for low-spatial skills students.
Speech and Eye Tracking Features for L2 Acquisition: A Multimodal Experiment

Spoken language variation analysis is increasingly considered in multimodal settings combining knowledge from computer, human and social sciences. This work focuses on second language (L2) acquisition via the study of linguistic variation combined with eye-tracking measures. Its goal is to model L2 pronunciation, to understand and to predict through AI techniques the related metacognitive information concerning reading strategies, text comprehension and L2 level. We present an experimental protocol involving a reading aloud setup, as well as first data collection to gather L2 speech with associated eye-tracking measures.
Dynamic Conversational Chatbot for Assessing Primary Students

Teaching necessitates a method of determining if learners are gaining the desired knowledge and skills. We believe that chatbot technology would be an excellent solution to this problem. Using our approach, the chatbot will assess the student by using questions and answers recorded in a question bank. Four approaches were taken to assess the students' answers against the model answer, using models such as Word2Vec, all-mpnet-base-v2, and Sense2Vec models with Word Mover's Distance algorithm or Cosine Similarity. After evaluating these approaches, the best performing approach was when we used the Sense2Vec model with Cosine Similarity which gave the most accurate similarity score range for correct and incorrect answers.
Using Genetic Programming and Linear Regression for Academic Performance Analysis

The academic evaluation process, even today, is the subject of much discussion. This process can use quantitative analysis to indicate the level of learning of students to support the decision about whether the student can attend the next curriculum phase. From this context, this paper analyzes the history of students' grades in the 1st year of a technical course in informatics integrated to high school, for the years 2020 and 2021, through the linear regression method, supported by genetic programming, to find out the influence of the grades of the first two bimesters concerning the final grade. The main results show that the genetic programming algorithm favored the search for linear regression models with a good fit to the datasets with students' data. The resultant models proved accurate and explained more than 74% of the datasets.
Bi-directional Mechanism for Recursion Algorithms: A Case Study on Gender Identification in MOOCs

Automatically identifying the learner gender, which serves as this paper's focus, can provide valuable information to personalised learners' experiences in MOOCs. However, extracting the gender from learner-generated data (discussion forum) is a challenging task, which is understudied in literature. Using syntactic features is still the state-of-the-art for gender identification in social media. Instead we propose here a novel approach based on Recursive Neural Networks (RecNN), to learn advanced syntactic knowledge extracted from learners' comments, as an NLP-based predictor for their gender identity. We propose a bi-directional composition function, added to NLP state-of-the-art candidate RecNN models. We evaluate different combinations of semantic level encoding and syntactic level encoding functions, exploring their performances, with respect to the task of learner gender profiling in MOOCs.
An Antibot-Based Web Voting System for Higher Institutions

The Internet has caused an evolution in how people socialize, work, and do business. The emergence and improvement in cloud computing and web technologies make interactions and remote processes possible. This advancement has presented an opportunity for the people and their representatives to meet during the voting process. Voting is making a choice or decision within a particular group. However, the conventional voting process that uses the paper-based approach faces the challenges of multiple voting, overvoting, cost, high voting fraud, and delay in declaring election results due to long counting times. Various methods have been proposed to overcome the multiple challenges prevalent in the traditional voting system. This paper proposes an antibot-based web voting platform that enables voters to vote within any location. It uses the hash technique and the antibot checking features to enforce security and voters’ confidentiality. PHP and HTML languages were used to implement the front-end of the system. SQL database and the Apache server were used for the back-end. On implementation and testing, our system shows good security enhancement and a reduction in the time consumed for counting and declaring election results.
Software Functional Requirements Classification Using Ensemble Learning

Software requirement classification is crucial in segregating the user requirements into functional and quality requirements, based on their feedback or client demand. Doing so manually is time-consuming and not feasible. This can lead to delays in satisfying the requirements which in turn can lead to unhappier clients and users. Thus, machine learning techniques are used to optimize this task. In this work, five different word embedding techniques have been applied to the functional and non-functional (quality) software requirements. SMOTE is used to balance the numerical data obtained after word embedding. Dimensionality reduction and feature selection techniques are then employed to eliminate redundant and irrelevant features. Principal Component Analysis (PCA) is used for dimensionality reduction, and Rank-Sum test (RST) is used for feature selection. The resulting vectors are fed as inputs to eight different classifiers- Bagged k-Nearest Neighbors, Bagged Decision Tree, Bagged Naive-Bayes, Random Forest, Extra Tree, Adaptive Boost, Gradient Boosting, and a Majority Voting ensemble classifier, with Decision Tree, k-Nearest Neighbors, and Gaussian Naive Bayes. The experimental results suggest that the combination of word embedding and feature selection techniques with the various classifiers are successful in accurately classifying functional and quality software requirements.
Using Generalizability Theory software suite: GENOVA, urGENOVA, and mGENOVA

This article reviews the GENOVA Suite designed for generalizability theory analyses. The GENOVA Suite consists of three programs: GENOVA, urGENOVA, and mGENOVA. Key features and capabilities of the programs are presented and two illustrative example analyses are provided using mGENOVA. Additionally, comparisons with some existing programs are made.