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Tae Yeon Kwon; A. Corinne Huggins-Manley; Jonathan Templin; Mingying Zheng – Journal of Educational Measurement, 2024
In classroom assessments, examinees can often answer test items multiple times, resulting in sequential multiple-attempt data. Sequential diagnostic classification models (DCMs) have been developed for such data. As student learning processes may be aligned with a hierarchy of measured traits, this study aimed to develop a sequential hierarchical…
Descriptors: Classification, Accuracy, Student Evaluation, Sequential Approach
Michael Schultz – Sociological Methods & Research, 2024
This paper presents a model of recurrent multinomial sequences. Though there exists a quite considerable literature on modeling autocorrelation in numerical data and sequences of categorical outcomes, there is currently no systematic method of modeling patterns of recurrence in categorical sequences. This paper develops a means of discovering…
Descriptors: Research Methodology, Sequential Approach, Models, Markov Processes
Tae Yeon Kwon; A. Corinne Huggins-Manley; Jonathan Templin; Mingying Zheng – Grantee Submission, 2023
In classroom assessments, examinees can often answer test items multiple times, resulting in sequential multiple-attempt data. Sequential diagnostic classification models (DCMs) have been developed for such data. As student learning processes may be aligned with a hierarchy of measured traits, this study aimed to develop a sequential hierarchical…
Descriptors: Classification, Accuracy, Student Evaluation, Sequential Approach
Aylward, Ronald C.; Cronjé, Johannes C. – Educational Technology Research and Development, 2022
The pedagogical paradigms of Direct instruction (behaviorism/objectivism) and Constructivism are often seen as opposing paradigms at the ends of an instructional design continuum. Unfortunately, this view makes the two approaches mutually exclusive. Designers must use the one at the expense of the other. A previous study proposed that the two…
Descriptors: Instructional Design, Behaviorism, Constructivism (Learning), Mastery Learning
Joaquín Cañero-Arias; Ángel Blanco-López; José María Oliva – International Journal of Science Education, 2024
This research integrates context-based learning and modelling. It presents a teaching-learning sequence (TLS) about the dissolution of gases in liquids using carbonated drinks as the context. The impact of the TLS is analysed in a longitudinal short-term study involving two groups of learners aged 13-14 years old (n=53). The results led us to…
Descriptors: Foreign Countries, Science Instruction, Models, Secondary School Science
Peter Curtis; Brett Moffett; David A. Martin – Australian Primary Mathematics Classroom, 2024
In this article, the authors explore how the 3C Model can be used to integrate other curriculum areas with mathematics, namely digital technologies. To illustrate the model, they provide a practical example of a teaching sequence. T he 3C Model is designed to create opportunities for applying reasoning and problem-solving skills and learning…
Descriptors: Models, Computer Software, Problem Solving, Mathematics Instruction
Zhan, Peida; He, Keren – Educational Measurement: Issues and Practice, 2021
In learning diagnostic assessments, the attribute hierarchy specifies a sequential network of interrelated attribute mastery processes, which makes a test blueprint consistent with the cognitive theory. One of the most important functions of attribute hierarchy is to guide or limit the developmental direction of students and then form a…
Descriptors: Longitudinal Studies, Models, Comparative Analysis, Diagnostic Tests
ALSaad, Fareedah; Reichel, Thomas; Zeng, Yuchen; Alawini, Abdussalam – International Educational Data Mining Society, 2021
With the emergence of MOOCs, it becomes crucial to automate the process of a course design to accommodate the diverse learning demands of students. Modeling the relationships among educational topics is a fundamental first step for automating curriculum planning and course design. In this paper, we introduce "Topic Transition Map" (TTM),…
Descriptors: Online Courses, Student Diversity, Student Needs, Course Content
Ziying Li; A. Corinne Huggins-Manley; Walter L. Leite; M. David Miller; Eric A. Wright – Educational and Psychological Measurement, 2022
The unstructured multiple-attempt (MA) item response data in virtual learning environments (VLEs) are often from student-selected assessment data sets, which include missing data, single-attempt responses, multiple-attempt responses, and unknown growth ability across attempts, leading to a complex and complicated scenario for using this kind of…
Descriptors: Sequential Approach, Item Response Theory, Data, Simulation
Khan, Md Akib Zabed; Polyzou, Agoritsa – International Educational Data Mining Society, 2023
Academic advising plays an important role in students' decision-making in higher education. Data-driven methods provide useful recommendations to students to help them with degree completion. Several course recommendation models have been proposed in the literature to recommend courses for the next semester. One aspect of the data that has yet to…
Descriptors: Course Selection (Students), Learning Analytics, Academic Advising, Decision Making
Zhou, Jianing; Bhat, Suma – Grantee Submission, 2021
Consistency of learning behaviors is known to play an important role in learners' engagement in a course and impact their learning outcomes. Despite significant advances in the area of learning analytics (LA) in measuring various self-regulated learning behaviors, using LA to measure consistency of online course engagement patterns remains largely…
Descriptors: Models, Online Courses, Learner Engagement, Learning Processes