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Download full textKhosravi, Hassan; Cooper, Kendra M. L. – Journal of Learning Analytics, 2018
Educational environments continue to evolve rapidly to address the needs of diverse, growing student populations while embracing advances in pedagogy and technology. In this changing landscape, ensuring consistency among the assessments for different offerings of a course (within or across terms), providing meaningful feedback about student…
Descriptors: Graphs, Academic Achievement, Student Evaluation, Models
Kleinman, Erica; Shergadwala, Murtuza N.; Teng, Zhaoqing; Villareale, Jennifer; Bryant, Andy; Zhu, Jichen; Seif El-Nasr, Magy – Journal of Learning Analytics, 2022
Educational technology is shifting toward facilitating personalized learning. Such personalization, however, requires a detailed understanding of students' problem-solving processes. Sequence analysis (SA) is a promising approach to gaining granular insights into student problem solving; however, existing techniques are difficult to interpret…
Descriptors: Problem Solving, Learning Analytics, Decision Making, Educational Technology
Victor Manuel Corza-Vargas; Roberto Martinez-Maldonado; Boris Escalante-Ramirez; Jimena Olveres – Journal of Learning Analytics, 2024
While teachers often monitor and adjust their learning design based on students' emotional states in physical classrooms, synchronous online environments often limit their ability to perceive the emotional climate of the class. Drawing from the concept of social translucence, it is suggested that making students' emotional states…
Descriptors: Foreign Countries, Undergraduate Students, Privacy, Cultural Awareness
Brown, Michael; DeMonbrun, R. Matthew; Teasley, Stephanie – Journal of Learning Analytics, 2018
In this study, we develop and test four measures for conceptualizing the potential impact of co-enrollment in different courses on students' changing risk for academic difficulty and recovery from academic difficulty in a focal course. We offer four predictors, two related to instructional complexity and two related to structural complexity (the…
Descriptors: At Risk Students, Dropout Prevention, Difficulty Level, College Curriculum
Casey, Kevin – Journal of Learning Analytics, 2017
Learning analytics offers insights into student behaviour and the potential to detect poor performers before they fail exams. If the activity is primarily online (for example computer programming), a wealth of low-level data can be made available that allows unprecedented accuracy in predicting which students will pass or fail. In this paper, we…
Descriptors: Keyboarding (Data Entry), Educational Research, Data Collection, Data Analysis
Méndez, Gonzalo; Ochoa, Xavier; Chiluiza, Katherine; de Wever, Bram – Journal of Learning Analytics, 2014
Learning analytics has been as used a tool to improve the learning process mainly at the micro-level (courses and activities). However, another of the key promises of learning analytics research is to create tools that could help educational institutions at the meso- and macro-level to gain better insight into the inner workings of their programs…
Descriptors: Data Analysis, Data Collection, Educational Research, Curriculum Design
