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Vongkulluksn, Vanessa W.; Xie, Kui – Open Education Studies, 2022
Learning processes often occur at a situational level. Changes in learning context have implications on how students are motivated or are able to cognitively process information. To study such situational phenomena, Experience Sampling Method (ESM) can help assess psychological variables in the moment and in context. However, data collected via…
Descriptors: Learning Processes, Sampling, Hierarchical Linear Modeling, Experience
Botarleanu, Robert-Mihai; Dascalu, Mihai; Allen, Laura K.; Crossley, Scott Andrew; McNamara, Danielle S. – Grantee Submission, 2022
Automated scoring of student language is a complex task that requires systems to emulate complex and multi-faceted human evaluation criteria. Summary scoring brings an additional layer of complexity to automated scoring because it involves two texts of differing lengths that must be compared. In this study, we present our approach to automate…
Descriptors: Automation, Scoring, Documentation, Likert Scales
Rodrigues, Rodrigo Lins; Ramos, Jorge Luis Cavalcanti; Silva, João Carlos Sedraz; Dourado, Raphael A.; Gomes, Alex Sandro – International Journal of Distance Education Technologies, 2019
The increasing use of the Learning Management Systems (LMSs) is making available an ever-growing, volume of data from interactions between teachers and students. This study aimed to develop a model capable of predicting students' academic performance based on indicators of their self-regulated behavior in LMSs. To accomplish this goal, the authors…
Descriptors: Management Systems, Teacher Student Relationship, Distance Education, College Students
Smith, Marlene A.; Kellogg, Deborah L. – Decision Sciences Journal of Innovative Education, 2015
This article describes a predictive model that assesses whether a student will have greater perceived learning in group assignments or in individual work. The model produces correct classifications 87.5% of the time. The research is notable in that it is the first in the education literature to adopt a predictive modeling methodology using data…
Descriptors: Group Activities, Assignments, Cooperative Learning, Individual Activities
Milligan, Colin; Fontana, Rosa Pia; Littlejohn, Allison; Margaryan, Anoush – Journal of Workplace Learning, 2015
Purpose: This paper aims to explore the role of self-regulatory behaviours in predicting workplace learning. As work practices in knowledge-intensive domains become more complex, individual workers must take greater responsibility for their ongoing learning and development. Design/methodology/approach: The study was conducted with knowledge…
Descriptors: Metacognition, Regression (Statistics), Role, Prediction
Akar, Sacide Guzin Mazman; Altun, Arif – Contemporary Educational Technology, 2017
The purpose of this study is to investigate and conceptualize the ranks of importance of social cognitive variables on university students' computer programming performances. Spatial ability, working memory, self-efficacy, gender, prior knowledge and the universities students attend were taken as variables to be analyzed. The study has been…
Descriptors: Individual Differences, Learning Processes, Programming, Self Efficacy
Tilfarlioglu, Filiz Yalçin; Anwer, Jivan Kamal – Journal of Education and Training Studies, 2017
Lean is regarded as a systematic approach to maximizing value by minimizing waste, and by flowing the product or service at the pull of the customer demand. These key concepts of "value," "flow," and "pull," align with the ultimate lean goal: "perfection," or a continuous striving for improvement in the…
Descriptors: Second Language Learning, Second Language Instruction, English (Second Language), Teaching Methods
Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol – Grantee Submission, 2015
Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…
Descriptors: Educational Environment, Predictive Measurement, Predictor Variables, Cooperative Learning
Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol – International Educational Data Mining Society, 2015
Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…
Descriptors: Educational Environment, Predictive Measurement, Predictor Variables, Cooperative Learning
Arslan, Ali – Educational Sciences: Theory and Practice, 2012
The purpose of this study is to reveal the extent to which the sources of 6th- 8th grade students' self-efficacy beliefs predict their self-efficacy beliefs for learning and performance. The study is correlational and was conducted on a total of 1049 students during the fall term of the educational year 2010-2011. The data of the study were…
Descriptors: Self Efficacy, Measures (Individuals), Correlation, Elementary School Students
Lagnado, David A.; Newell, Ben R.; Kahan, Steven; Shanks, David R. – Journal of Experimental Psychology: General, 2006
In multiple-cue learning (also known as probabilistic category learning) people acquire information about cue-outcome relations and combine these into predictions or judgments. Previous researchers claimed that people can achieve high levels of performance without explicit knowledge of the task structure or insight into their own judgment…
Descriptors: Learning Strategies, Cues, Performance, Prediction
Pechenizkiy, Mykola; Calders, Toon; Conati, Cristina; Ventura, Sebastian; Romero, Cristobal; Stamper, John – International Working Group on Educational Data Mining, 2011
The 4th International Conference on Educational Data Mining (EDM 2011) brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large datasets to answer educational research questions. The conference, held in Eindhoven, The Netherlands, July 6-9, 2011, follows the three previous editions…
Descriptors: Academic Achievement, Logical Thinking, Profiles, Tutoring

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