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Prasoon Patidar; Tricia J. Ngoon; Neeharika Vogety; Nikhil Behari; Chris Harrison; John Zimmerman; Amy Ogan; Yuvraj Agarwal – Journal of Learning Analytics, 2024
Classroom sensing systems can capture data on teacher-student behaviours and interactions at a scale far greater than human observers can. These data, translated to multi-modal analytics, can provide meaningful insights to educational stakeholders. However, complex data can be difficult to make sense of. In addition, analyses done on these data…
Descriptors: Learning Analytics, Classroom Observation Techniques, Data Analysis, Student Behavior
Yangyang Luo; Xibin Han; Chaoyang Zhang – Asia Pacific Education Review, 2024
Learning outcomes can be predicted with machine learning algorithms that assess students' online behavior data. However, there have been few generalized predictive models for a large number of blended courses in different disciplines and in different cohorts. In this study, we examined learning outcomes in terms of learning data in all of the…
Descriptors: Prediction, Learning Management Systems, Blended Learning, Classification
Singelmann, Lauren Nichole – ProQuest LLC, 2022
To meet the national and international call for creative and innovative engineers, many engineering departments and classrooms are striving to create more authentic learning spaces where students are actively engaging with design and innovation activities. For example, one model for teaching innovation is Innovation-Based Learning (IBL) where…
Descriptors: Engineering Education, Design, Educational Innovation, Models
Sointu, Erkko; Saqr, Mohammed; Valtonen, Teemu; Hallberg, Susanne; Väisänen, Sanna; Kankaanpää, Jenni; Tuominen, Ville; Hirsto, Laura – Journal of Technology and Teacher Education, 2023
Pre-service teacher training is research intensive in Finland. Additionally, teaching as a profession is highly valued among young people. However, quantitative methods courses are challenging for teacher students from many reasons. Particularly, this is due to previous negative experiences and emotions (among other things). Thus, novel approaches…
Descriptors: Emotional Response, Preservice Teachers, Student Behavior, Difficulty Level
Ean Teng Khor; Dave Darshan – International Journal of Information and Learning Technology, 2024
Purpose: This study leverages social network analysis (SNA) to visualise the way students interacted with online resources and uses the data obtained from SNA as features for supervised machine learning algorithms to predict whether a student will successfully complete a course. Design/methodology/approach: The exploration and visualisation of the…
Descriptors: Prediction, Academic Achievement, Electronic Learning, Artificial Intelligence
Papamitsiou, Zacharoula; Economides, Anastasios A. – Journal of Computer Assisted Learning, 2021
This longitudinal study investigates the differences in learners' effortful behaviour over time due to receiving metacognitive help--in the form of on-demand task-related visual analytics. Specifically, learners' interactions (N = 67) with the tasks were tracked during four self-assessment activities, conducted at four discrete points in time,…
Descriptors: Metacognition, Help Seeking, Learning Analytics, Student Behavior
Susnjak, Teo; Ramaswami, Gomathy Suganya; Mathrani, Anuradha – International Journal of Educational Technology in Higher Education, 2022
This study investigates current approaches to learning analytics (LA) dashboarding while highlighting challenges faced by education providers in their operationalization. We analyze recent dashboards for their ability to provide actionable insights which promote informed responses by learners in making adjustments to their learning habits. Our…
Descriptors: Learning Analytics, Computer Interfaces, Artificial Intelligence, Prediction
Hellings, Jan; Haelermans, Carla – Higher Education: The International Journal of Higher Education Research, 2022
We use a randomised experiment to study the effect of offering half of 556 freshman students a learning analytics dashboard and a weekly email with a link to their dashboard, on student behaviour in the online environment and final exam performance. The dashboard shows their online progress in the learning management systems, their predicted…
Descriptors: Learning Analytics, College Freshmen, Student Behavior, Electronic Learning
Quadir, Benazir; Chen, Nian-Shing; Isaias, Pedro – Interactive Learning Environments, 2022
The purpose of this study is to review journal papers on educational big data research published from 2010 to 2018. A total of 143 papers were selected. The papers were characterized based on three dimensions: (a) educational goals; (b) educational problems addressed; and (c) big data analytical techniques used. A qualitative content analysis…
Descriptors: Data, Educational Research, Educational Objectives, Data Analysis
Aom Perkash; Qaisar Shaheen; Robina Saleem; Furqan Rustam; Monica Gracia Villar; Eduardo Silva Alvarado; Isabel de la Torre Diez; Imran Ashraf – Education and Information Technologies, 2024
Developing tools to support students, educators, intuitions, and government in the educational environment has become an important task to improve the quality of education and learning outcomes. Information and communication technology (ICT) is adopted by educational institutions; one such instance is video interaction in flipped teaching.…
Descriptors: Academic Achievement, Colleges, Artificial Intelligence, Predictor Variables
Denis Zhidkikh; Ville Heilala; Charlotte Van Petegem; Peter Dawyndt; Miitta Jarvinen; Sami Viitanen; Bram De Wever; Bart Mesuere; Vesa Lappalainen; Lauri Kettunen; Raija Hämäläinen – Journal of Learning Analytics, 2024
Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1). General-purpose and interpretable dropout predictions still pose a challenge. Our study aims to reproduce and extend the data analysis of a privacy-first…
Descriptors: Learning Analytics, Prediction, School Holding Power, Academic Achievement
Kaliisa, Rogers; Dolonen, Jan Arild – Technology, Knowledge and Learning, 2023
Despite the potential of learning analytics (LA) to support teachers' everyday practice, its adoption has not been fully embraced due to the limited involvement of teachers as co-designers of LA systems and interventions. This is the focus of the study described in this paper. Following a design-based research (DBR) approach and guided by concepts…
Descriptors: College Faculty, Student Participation, Discourse Analysis, Behavior Patterns
So, Joseph Chi-Ho; Ho, Yik Him; Wong, Adam Ka-Lok; Chan, Henry C. B.; Tsang, Kia Ho-Yin; Chan, Ada Pui-Ling; Wong, Simon Chi-Wang – IEEE Transactions on Learning Technologies, 2023
Generic competence (GC) development is an integral part of higher education to provide holistic education and enhance student career development. It also plays a critical role in complementing the curriculum. Many tertiary institutions provide various GC development activities (GCDA). Moreover, institutions strongly need to further understand…
Descriptors: Predictor Variables, Higher Education, Online Courses, Correlation
Gomathy Ramaswami; Teo Susnjak; Anuradha Mathrani – Journal of Learning Analytics, 2023
Learning Analytics Dashboards (LADs) are gaining popularity as a platform for providing students with insights into their learning behaviour patterns in online environments. Existing LAD studies are mainly centred on displaying students' online behaviours with simplistic descriptive insights. Only a few studies have integrated predictive…
Descriptors: Learner Engagement, Learning Analytics, Electronic Learning, Student Behavior
Mutimukwe, Chantal; Viberg, Olga; Oberg, Lena-Maria; Cerratto-Pargman, Teresa – British Journal of Educational Technology, 2022
Understanding students' privacy concerns is an essential first step toward effective privacy-enhancing practices in learning analytics (LA). In this study, we develop and validate a model to explore the students' privacy concerns (SPICE) regarding LA practice in higher education. The SPICE model considers "privacy concerns" as a central…
Descriptors: Privacy, Learning Analytics, Student Attitudes, College Students

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