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Aditya Upadhyayula; Neil Cohn – Cognitive Science, 2025
Theories of visual narrative comprehension have advocated for a hierarchical grammar-based comprehension mechanism, but only limited work has investigated this hierarchy. Here, we provide a computational framework inspired by computational psycholinguistics to address hierarchy in visual narratives. The predictions generated by this framework were…
Descriptors: Visual Perception, Comprehension, Vertical Organization, Story Grammar
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Esteban Villalobos; Mar Perez-Sanagustin; Roger Azevedo; Cedric Sanza; Julien Broisin – IEEE Transactions on Learning Technologies, 2024
Blended learning (BL) has become increasingly popular in higher education institutions. Despite its popularity and the advances in methodologies for the detection of learning tactics and strategies from trace data, little is known about how they apply to BL settings and, therefore, how students use them to plan, organize, monitor, and regulate…
Descriptors: Metacognition, Learning Strategies, Blended Learning, Instructional Design
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Xia, Xiaona – Interactive Learning Environments, 2023
Learning interaction activities are the key part of tracking and evaluating learning behaviors, that plays an important role in data-driven autonomous learning and optimized learning in interactive learning environments. In this study, a big data set of learning behaviors with multiple learning periods is selected. According to the instance…
Descriptors: Behavior, Learning Processes, Electronic Learning, Algorithms
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Almorza, D.; Prada, A.; Kandús, M. V.; Salerno, J. C. – Journal of Biological Education, 2023
Graduates in biology or genetics learn Mendel's laws and Hardy-Weinberg equilibrium as students, and they know, use, and sometimes teach these concepts. However, it is unusual to learn about stochastic processes during the graduate studies of these topics, although the applications of Markov chains in the fields of genetics or biology are…
Descriptors: Graduate Students, College Science, Biology, Genetics
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Hatice Yildiz Durak – Education and Information Technologies, 2025
Feedback is critical in providing personalized information about educational processes and supporting their performance in online collaborative learning environments. However, giving effective feedback and monitoring its effects, which is especially important in online environments, is a complex issue. Although providing feedback by analyzing…
Descriptors: Feedback (Response), Online Systems, Electronic Learning, Learning Analytics
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Hongnan Wan – International Association for Development of the Information Society, 2022
The teaching quality evaluation of college teachers is an important indicator of school evaluation, performance assessment and teacher promotion. However, the existing evaluation methods are only limited to the formal evaluation of teaching behavior, and have not achieved objective, reasonable and scientific evaluation results. The purpose of this…
Descriptors: Teacher Behavior, Teacher Effectiveness, Teacher Evaluation, College Faculty
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Boumi, Shahab; Vela, Adan Ernesto – Education Sciences, 2020
American universities use a procedure based on a rolling six-year graduation rate to calculate statistics regarding their students' final educational outcomes (graduating or not graduating). As an alternative to the six-year graduation rate method, many studies have applied absorbing Markov chains for estimating graduation rates. In both cases, a…
Descriptors: Graduation Rate, Computation, Markov Processes, Accuracy
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Christhilf, Katerina; Newton, Natalie; Butterfuss, Reese; McCarthy, Kathryn S.; Allen, Laura K.; Magliano, Joseph P.; McNamara, Danielle S. – International Educational Data Mining Society, 2022
Prompting students to generate constructed responses as they read provides a window into the processes and strategies that they use to make sense of complex text. In this study, Markov models examined the extent to which: (1) patterns of strategies; and (2) strategy combinations could be used to inform computational models of students' text…
Descriptors: Markov Processes, Reading Strategies, Reading Comprehension, Models
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Kumar, Amruth N. – International Educational Data Mining Society, 2023
Is there a pattern in how students solve Parsons puzzles? Is there a difference between the puzzle-solving strategies of C++ and Java students? We used Markov transition matrix to answer these questions. We analyzed the solutions of introductory programming students solving Parsons puzzles involving if-else statements and while loops in C++ and…
Descriptors: Markov Processes, Puzzles, Introductory Courses, Computer Science Education
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Tang, Hengtao; Dai, Miao; Yang, Shuoqiu; Du, Xu; Hung, Jui-Long; Li, Hao – Distance Education, 2022
The purpose of this research was to apply multimodal learning analytics in order to systemically investigate college students' attention states during their collaborative problem-solving (CPS) in online settings. Existing research on CPS relies on self-reported data, which limits the validity of the findings. This study looked at data in a…
Descriptors: Learning Analytics, College Students, Attention, Cooperative Learning
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Lokkila, Erno; Christopoulos, Athanasios; Laakso, Mikko-Jussi – Informatics in Education, 2023
Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student…
Descriptors: Prior Learning, Programming, Computer Science Education, Markov Processes
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Williamson, Kimberly; Kizilcec, René F. – International Educational Data Mining Society, 2021
Knowledge tracing algorithms such as Bayesian Knowledge Tracing (BKT) can provide students and teachers with helpful information about their progress towards learning objectives. Despite the popularity of BKT in the research community, the algorithm is not widely adopted in educational practice. This may be due to skepticism from users and…
Descriptors: Bayesian Statistics, Learning Processes, Computer Software, Learning Analytics
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Johnson, Marina E.; Misra, Ram; Berenson, Mark – Decision Sciences Journal of Innovative Education, 2022
In the era of artificial intelligence (AI), big data (BD), and digital transformation (DT), analytics students should gain the ability to solve business problems by integrating various methods. This teaching brief illustrates how two such methods--Bayesian analysis and Markov chains--can be combined to enhance student learning using the Analytics…
Descriptors: Bayesian Statistics, Programming Languages, Artificial Intelligence, Data Analysis
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Boumi, Shahab; Vela, Adan – International Educational Data Mining Society, 2019
Simplified categorizations have often led to college students being labeled as full-time or part-time students. However, at many universities student enrollment patterns can be much more complicated, as it is not uncommon for students to alternate between full-time and part-time enrollment each semester based on finances, scheduling, or family…
Descriptors: Markov Processes, Enrollment, College Students, Full Time Students
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Ahmad Uzir, Nora'ayu; Gaševic, Dragan; Matcha, Wannisa; Jovanovic, Jelena; Pardo, Abelardo – Journal of Computer Assisted Learning, 2020
This paper aims to explore time management strategies followed by students in a flipped classroom through the analysis of trace data. Specifically, an exploratory study was conducted on the dataset collected in three consecutive offerings of an undergraduate computer engineering course (N = 1,134). Trace data about activities were initially coded…
Descriptors: Time Management, Blended Learning, Learning Analytics, Undergraduate Students
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