Reviewerly/exHarmony huggingface dataset:
Recurrence Quantification Analysis of Eye Gaze Dynamics During Team Collaboration

Shared visual attention between team members facilitates collaborative problem solving (CPS), but little is known about how team-level eye gaze dynamics influence the quality and successfulness of CPS. To better understand the role of shared visual attention during CPS, we collected eye gaze data from 279 individuals solving computer-based physics puzzles while in teams of three. We converted eye gaze into discrete screen locations and quantified team-level gaze dynamics using recurrence quantification analysis (RQA). Specifically, we used a centroid-based auto-RQA approach, a pairwise team member cross-RQAs approach, and a multi-dimensional RQA approach to quantify team-level eye gaze dynamics from the eye gaze data of team members. We find that teams differing in composition based on prior task knowledge, gender, and race show few differences in team-level eye gaze dynamics. We also find that RQA metrics of team-level eye gaze dynamics were predictive of task success (all ps < .001). However, the same metrics showed different patterns of feature importance depending on predictive model and RQA type, suggesting some redundancy in task-relevant information. These findings signify that team-level eye gaze dynamics play an important role in CPS and that different forms of RQA pick up on unique aspects of shared attention between team-members.
Each Encounter Counts: Modeling Language Learning and Forgetting

Language learning applications usually estimate the learner's language knowledge over time to provide personalized practice content for each learner at the optimal timing. However, accurately predicting language knowledge or linguistic skills is much more challenging than math or science knowledge, as many language tasks involve memorization and retrieval. Learners must memorize a large number of words and meanings, which are prone to be forgotten without practice. Although a few studies consider forgetting when modeling learners' language knowledge, they tend to apply traditional models, consider only partial information about forgetting, and ignore linguistic features that may significantly influence learning and forgetting. This paper focuses on modeling and predicting learners' knowledge by considering their forgetting behavior and linguistic features in language learning. Specifically, we first explore the existence of forgetting behavior and cross-effects in real-world language learning datasets through empirical studies. Based on these, we propose a model for predicting the probability of recalling a word given a learner's practice history. The model incorporates key information related to forgetting, question formats, and semantic similarities between words using the attention mechanism. Experiments on two real-world datasets show that the proposed model improves performance compared to baselines. Moreover, the results indicate that combining multiple types of forgetting information and item format improves performance. In addition, we find that incorporating semantic features, such as word embeddings, to model similarities between words in a learner's practice history and their effects on memory also improves the model.
Detecting the Disengaged Reader - Using Scrolling Data to Predict Disengagement during Reading

When reading long and complex texts, students may disengage and miss out on relevant content. In order to prevent disengaged behavior or to counteract it by means of an intervention, it is ideally detected an early stage. In this paper, we present a method for early disengagement detection that relies only on the classification of scrolling data. The presented method transforms scrolling data into a time series representation, where each point of the series represents the vertical position of the viewport in the text document. This time series representation is then classified using time series classification algorithms. We evaluated the method on a dataset of 565 university students reading eight different texts. We compared the algorithm performance with different time series lengths, data sampling strategies, the texts that make up the training data, and classification algorithms. The method can classify disengagement early with up to 70% accuracy. However, we also observe differences in the performance depending on which of the texts are included in the training dataset. We discuss our results and propose several possible improvements to enhance the method.
Predicting Item Response Theory Parameters Using Question Statements Texts

Recently, new Neural Language Models pre-trained on a massive corpus of texts are available. These models encode statistical features of the languages through their parameters, creating better word vector representations that allow the training of neural networks with smaller sample sets. In this context, we investigate the application of these models to predict Item Response Theory parameters in multiple choice questions. More specifically, we apply our models for the Brazilian National High School Exam (ENEM) questions using the text of their statements and propose a novel optimization target for regression: Item Characteristic Curve. The architecture employed could predict the difficulty parameter b of the ENEM 2020 and 2021 items with a mean absolute error of 70 points. Calculating the IRT score in each knowledge area of the exam for a sample of 100,000 students, we obtained a mean absolute below 40 points for all knowledge areas. Considering only the top quartile, the exam's main target of interest, the average error was less than 30 points for all areas, being the majority lower than 15 points. Such performance allows predicting parameters on newly created questions, composing mock tests for student training, and analyzing their performance with excellent precision, dispensing with the need for costly item calibration pre-test step.
Instructional Strategies and Student eTextbook Reading

Students' reading is an essential part of learning in college courses. However, many instructors are concerned that students do not complete assigned readings, and multiple studies have found evidence to support this concern. A handful of studies suggest adopting strategies to address students' lack of reading. This research examines various instructional strategies and student eTextbook reading behaviors validated by page view data. Survey responses related to use of instructional strategies were collected. A total of 86 instructors from four public universities participated. Of these participants, 59 submitted the assigned reading pages for their courses. This resulted in reading data from 3,714 students which were examined in this study. The findings indicated that students read about 37% of the assigned pages on any given day during the semester. Also, of the students that read, two-thirds made at least one annotation and students tend to re-read the pages they annotated. Most importantly, student reading in the courses where strategies were used was almost three times higher than in the courses where no strategies were implemented.
Grant Activities and Sustainability of Higher Education Institutions: Evidence from Russia

This paper presents the results of a quantitative analysis of the contribution of grant activities to ensuring the sustainable development of higher education institutions in the Russian Federation. The authors consider sustainability in five aspects of activity: educational, research, international, financial and social. Results of higher educational institutions performance monitoring (2014-2022) formed the empirical basis of the study. In this research, we used the quantile regression model to identify the effects of inertial development and the effects associated with grant activities in the dynamics of universities performance indicators. We estimated the model parameters separately in each of the decile groups. Thus, we tested the central hypothesis of the study about the quantile heterogeneity of the sensitivity of university target indicators to the intensity and effectiveness of grant funding. This paper provides evidence of significant contributions the inertial nature of the development of universities, representing advanced development groups, into the sustainability of target performance indicators. However, in terms of sensitivity to the effectiveness and intensity of grant activities, they are inferior to universities from catch-up development groups in almost all aspects of their activities. The identified patterns of sensitivity to grant funding indicate a high potential for convergence (“catch-up development effect”) in the higher education system of the Russian Federation. These patterns can improve the effectiveness of management decisions in higher education by focusing on growth points with high sensitivity to grant support.
Everyday Cyber Safety for Students

Cybersecurity is a fast-evolving topic. There are many approaches and technologies designed to protect students. The jargon quickly becomes overpowering in exploring what it is and what to do about it. This chapter provides basic information that is accessible to students. We will explore cybersecurity from the perspective of the National Institute of Standards and Technology (NIST) framework. This model uses the topic areas of identify, protect, detect, respond and recover to create an environment of everyday cyber safety. The chapter then goes on to describe how cybercriminals influence behavior. This knowledge will help students identify cybercriminal behavior and become more cyber safe. The chapter provides tips on actions students can take to secure their environment. After completing this chapter, students will have the requisite cybersecurity vocabulary to recognize personal cyber safety requirements and turn that knowledge into action.
Reporting less coincidental similarity to educate students about programming plagiarism and collusion

ABSTRACTABSTRACTBackground and Context To educate students about programming plagiarism and collusion, we introduced an approach that automatically reports how similar a submitted program is to others. However, as most students receive similar feedback, those who engage in plagiarism and collusion might feel inadequately warned.Objective When students are likely to be engaging in plagiarism or collusion, we would like the system to apply enough pressure on them to make them reconsider their actions.Method This study proposes a variation of the approach, which is less likely to report coincidental similarity. The variation was compared with its predecessor via quasi-experiments with 202 computing students.Findings Students with the new approach are slightly more aware of programming plagiarism and collusion than those with the previous approach with a reduction in cases of such misconduct.Implications There is another way to automatically educate students about programming plagiarism and collusion with appropriate pressure.KEYWORDS: Program similarityplagiarismcollusionacademic integritycomputing education Disclosure statementNo potential conflict of interest was reported by the authors.Correction StatementThis article has been republished with minor changes. These changes do not impact the academic content of the article.
Dimension-wise Sequential Update for Learning a Multidimensional Environment in Humans

When confronted with multidimensional environment problems, humans may need to jointly update multiple state-action-outcome associations across various dimensions. Computational modeling of human behavior and neural activities suggests that such updates are implemented based upon Bayesian update principle. However, it is unclear whether humans perform these updates individually or sequentially. If the update occurs sequentially, the order in which the associations are updated matters and can influence the updated results. To address this question, we tested a few computational models with different update orders using both human behavior and EEG data. Our results indicated that a model undertaking dimension-wise sequential updates was the best fit to human behavior. In this model, ordering the dimensions was decided using entropy, which indexed the uncertainty of associations. Simultaneously collected EEG data revealed evoked potentials that were correlated to the proposed timing of this model. These findings provide new insights into the temporal processes underlying Bayesian update in multidimensional environments.
Reliable adaptive distributed hyperparameter optimization (RadHPO) for deep learning training and uncertainty estimation

Training and validation of Neural Networks (NN) are very computationally intensive. In this paper, we propose a distributed system based NN infrastructure that achieves two goals: to accelerate model training, specifically for hyperparameter optimization, and to re-use some of these intermediate models to evaluate the uncertainty of the model. By accelerating model training, we can obtain a large set of potential models and compare them in a shorter amount of time. Automating this process reduces development time and provides an easy way to compare different models. Our application runs different models on distinct servers with a single training data set, each with tweaked hyperparameters. By adding uncertainty to our results, our framework provides not just a single prediction but a distribution over predictions. Adding uncertainty is essential to some NN applications since most models assume that the input data distributions are identical between test and validation. However, in reality, they are producing some catastrophic mistakes. Since our solution is a distributed system, we make our implementation robust to common distributed system failures (servers going down, loss of communication among some nodes, and others). Furthermore, we use a gossip-style heartbeat protocol for failure detection and recovery. Finally, some preliminary results using a black-box approach to generate the training models show that our infrastructure scales well in different hardware platforms.
Vaccine opinion clustering in Indonesia using K-means algorithm and elbow method

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Faqih Hamami, Inayatul Fithriyah; Vaccine opinion clustering in Indonesia using K-means algorithm and elbow method. AIP Conf. Proc. 21 February 2023; 2482 (1): 100019. https://doi.org/10.1063/5.0128945 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Evaluation of the Universitas Jenderal Soedirman e-learning website (Eldiru) using the WebQual 4.0 method

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Nurul Hidayat, Lasmedi Afuan, Siti Nurhayati, Yogiek Indra Kurniawan; Evaluation of the Universitas Jenderal Soedirman e-learning website (Eldiru) using the WebQual 4.0 method. AIP Conf. Proc. 21 February 2023; 2482 (1): 100002. https://doi.org/10.1063/5.0110501 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Robust audio watermarking via quantization and particle swarm optimization

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Thoriq Bayu Aji, Jangkung Raharjo, Ledya Novamizanti, Mutiarahmi Khairunnisa; Robust audio watermarking via quantization and particle swarm optimization. AIP Conf. Proc. 21 February 2023; 2482 (1): 150003. https://doi.org/10.1063/5.0110459 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Recognition of Bima script handwriting patterns using the local binary pattern feature extraction method and K-nearest neighbour classification method

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Muhammad Ilham Fidatama, Fitri Bimantoro, Gibran Satya Nugraha, Budi Irmawati, Ramaditia Dwiyansaputra; Recognition of Bima script handwriting patterns using the local binary pattern feature extraction method and K-nearest neighbour classification method. AIP Conf. Proc. 21 February 2023; 2482 (1): 020009. https://doi.org/10.1063/5.0111770 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Employee welfare financing system with support vector machine and Naïve Bayes to Syariah banking

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Opitasari, Yaddarabullah, Dana Indra Sensuse; Employee welfare financing system with support vector machine and Naïve Bayes to Syariah banking. AIP Conf. Proc. 21 February 2023; 2482 (1): 100005. https://doi.org/10.1063/5.0111450 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
The development of autonomous drone for document delivery in IPB University

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Carli Apriansyah Hutagalung, Karlisa Priandana, Kudang Boro Seminar; The development of autonomous drone for document delivery in IPB University. AIP Conf. Proc. 21 February 2023; 2482 (1): 040001. https://doi.org/10.1063/5.0110699 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Bima script handwriting pattern recognition with gray level co-occurrence matrix feature extraction and zoning &amp; classification of probabilistic neural networks

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Muhammad Naufal, Fitri Bimantoro, Arik Aranta, I. Gede Pasek Suta Wijaya; Bima script handwriting pattern recognition with gray level co-occurrence matrix feature extraction and zoning & classification of probabilistic neural networks. AIP Conf. Proc. 21 February 2023; 2482 (1): 020010. https://doi.org/10.1063/5.0111802 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Teachers’ Challenges and Strategies in Teaching Literacy Skills for Children with Special Needs

This research is motivated by the existence of various literacy problems in kindergarten and elementary school. The purpose of this research is to find out the challenges faced by teachers in teaching literacy to children and the teacher's strategy in teaching literacy to children with special needs. This research is qualitative research in which the data is collected through open-ended questionnaires and interviews and analyzed descriptively. Result:1) Not all teachers accompanying children with special needs have adequate knowledge in teaching basic literacy skills; teachers find it difficult to find appropriate learning programs and media to assist children with different abilities,2) Strategies to teach basic literacy: implementing adaptive learning programs, providing supporting facilities; using differentiated learning; teaching pre-academic abilities; learning with nature and all the senses in the teachable moment. Early childhood education is the formation period for preparing children to study in elementary school. Therefore, a collaboration between kindergarten and elementary teachers in teaching literacy is required
Cobalt enrichment in some ores of the Bergslagen province, south central Sweden

The presence of cobalt (Co) in sulphide deposits or sulphide-bearing iron oxide deposits in and around the Palaeoproterozoic Bergslagen ore province, south central Sweden, led to focused mining and extraction of this metal, particularly during the nineteenth century. Today, Co is considered a critical metal in the EU and among the more sought-after raw materials, not least due to its use in batteries for the rapidly increasing production of electric vehicles. Here we report new observations and data on Co concentrations in variably mineralised and not necessarily statistically representative samples from a suite of mainly skarn-hosted, at least locally, sulphide-dominated mineralisations from Bergslagen. While several localities that exhibit substantial Co concentrations represent deposits previously known to carry this metal (generally in a field/district or specifically in a mine), the majority are from mines or prospects in which the presence of Co have been hitherto unknown. Several of them share the enrichment of, e.g., Co and Cu, but the overall picture is one of more complex interrelations between the variable metal endowments in the known occurrences of Co in this province. While representing a modest dataset, our new observations complement previously available information on the occurrence of Co in Bergslagen and highlights both the need for, and potential of, new and more detailed studies on the distribution, mineralogy and origin of Co as well as other critical or near-critical metals in this and other ore provinces in Sweden.
Dual-path recommendation algorithm based on CNN and attention-enhanced LSTM

To recommend useful information to users more efficiently, this paper proposes a dual-path recommendation algorithm which combines multilayer Convolutional Neural Network (CNN) and attention-enhanced long short-term memory network (Attention-LSTM). Firstly, the matrix factorisation technique is used for learning the long-term preferences of users. Secondly, a dual-path network based on CNN and LSTM is constructed to perform feature extraction on the rating matrix. The dual-path network can learn the long-term preferences of users while capturing their dynamic preferences in changing preferences. The algorithm is tested on the public dataset MovieLens-1M, and the MAE value reflects the accuracy of the algorithm.