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Anagha Vaidya; Sarika Sharma – Interactive Technology and Smart Education, 2024
Purpose: Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome. Learning analytics and educational data mining provide a set of techniques that can be conveniently applied to extensive data collected as part of the evaluation…
Descriptors: Course Evaluation, Learning Analytics, Formative Evaluation, Information Retrieval
Ramaswami, Gomathy; Susnjak, Teo; Mathrani, Anuradha; Umer, Rahila – Technology, Knowledge and Learning, 2023
Learning analytics dashboards (LADs) provide educators and students with a comprehensive snapshot of the learning domain. Visualizations showcasing student learning behavioral patterns can help students gain greater self-awareness of their learning progression, and at the same time assist educators in identifying those students who may be facing…
Descriptors: Prediction, Learning Analytics, Learning Management Systems, Identification
Liu, Zhi; Kong, Xi; Chen, Hao; Liu, Sannyuya; Yang, Zongkai – IEEE Transactions on Learning Technologies, 2023
In a massive open online courses (MOOCs) learning environment, it is essential to understand students' social knowledge constructs and critical thinking for instructors to design intervention strategies. The development of social knowledge constructs and critical thinking can be represented by cognitive presence, which is a primary component of…
Descriptors: MOOCs, Cognitive Processes, Students, Models
Xiaona Xia; Wanxue Qi – European Journal of Education, 2025
Massive Open Online Courses (MOOCs) effectively support online learning behaviour; while constructing a sustainable learning process, MOOCs have also formed the social network. In addition, learners' burnout state has become a serious obstacle to the development and promotion of MOOCs. This study analyzes the potential social behaviour associated…
Descriptors: MOOCs, Burnout, Social Behavior, Feedback (Response)
Patterson, Chris R.; York, Emily; Maxham, Danielle; Molina, Rudy; Mabrey, Paul, III – Journal of Learning Analytics, 2023
The anticipation, inclusion, responsiveness, and reflexivity (AIRR) framework (Stilgoe et al., 2013) is a novel framework that has helped those in science and technology fields shift their focus from products to the processes used to create those products. However, the framework has not been known to be applied to the development and…
Descriptors: Learning Analytics, Innovation, School Holding Power, At Risk Students
Qi Zhou; Wannapon Suraworachet; Mutlu Cukurova – Education and Information Technologies, 2024
Collaboration is argued to be an important skill, not only in schools and higher education contexts but also in the workspace and other aspects of life. However, simply asking students to work together as a group on a task does not guarantee success in collaboration. Effective collaborative learning requires meaningful interactions among…
Descriptors: Learning Analytics, Cooperative Learning, Nonverbal Communication, Speech Communication
Treice de Oliveira Moreira; Cláudio Azevedo Passos; Flávio Roberto Matias da Silva; Paulo Márcio Souza Freire; Isabel Fernandes de Souza; Cláudia Rödel Bosaipo Sales da Silva; Ronaldo Ribeiro Goldschmidt – Education and Information Technologies, 2024
The problem of propagating disinformation (a.k.a. "fake news") on social media has increased significantly in the last few years. There are several initiatives around the world to combat this serious problem. Maybe the most promising ones involve training people to identify "fake news." The use of digital educational games…
Descriptors: Deception, News Reporting, Misinformation, Portuguese
Kelly Linden; Neil van der Ploeg; Noelia Roman – Journal of Higher Education Policy and Management, 2023
There is a small window of opportunity at the beginning of semester for a university to provide commencing students with timely and targeted support. However, there is limited information available on interventions that identify and support disengaged students from equity groups without using equity group status as the basis for the contact. The…
Descriptors: Learner Engagement, Identification, Intervention, Learning Analytics
Davies, Randall; Allen, Gove; Albrecht, Conan; Bakir, Nesrin; Ball, Nick – Education Sciences, 2021
Analyzing the learning analytics from a course provides insights that can impact instructional design decisions. This study used educational data mining techniques, specifically a longitudinal k-means cluster analysis, to identify the strategies students used when completing the online portion of an online flipped spreadsheet course. An analysis…
Descriptors: Data Analysis, Identification, Learning Strategies, Electronic Learning
Ben Soussia, Amal; Labba, Chahrazed; Roussanaly, Azim; Boyer, Anne – International Journal of Information and Learning Technology, 2022
Purpose: The goal is to assess performance prediction systems (PPS) that are used to assist at-risk learners. Design/methodology/approach: The authors propose time-dependent metrics including earliness and stability. The authors investigate the relationships between the various temporal metrics and the precision metrics in order to identify the…
Descriptors: Performance, Prediction, Student Evaluation, At Risk Students
Joseph, Lumy; Abraham, Sajimon; Mani, Biju P.; N., Rajesh – Journal of Educational Computing Research, 2022
A fixed learning path for all learners is a major drawback of virtual learning systems. An online learning path recommendation system has the advantage of offering flexibility to select appropriate learning content. Learning Analytics Intervention (LAI) provides several educational benefits, particularly for low-performing students. Researchers…
Descriptors: Cognitive Style, Learning Analytics, Educational Benefits, Integrated Learning Systems
Frances Edwards; Bronwen Cowie; Suzanne Trask – Professional Development in Education, 2025
This paper reports on teachers developing their own data literacy and then acting as data coaches for colleagues in their schools. The 13 teachers from 7 schools in the study analysed standardised data using a data conversation protocol to identify students with significant mathematical misconceptions. They then took data-informed action with…
Descriptors: Coaching (Performance), Peer Teaching, Statistics Education, Knowledge Level
Iouri Kotorov; Yuliya Krasylnykova; Mar Pérez-Sanagustín; Fernanda Mansilla; Julien Broisin – Journal of Learning Analytics, 2024
The quality of the data and the amount of correct information available is key to informed decision-making. Higher education institutions (HEIs) often employ various decision support systems (DSSs) to make better choices. However, there is a lack of systems to assist with decision-making to promote innovation in teaching and learning. In this…
Descriptors: Decision Making, Case Studies, Instructional Innovation, Teaching Methods
Rosemary Vellar; Boris Handal; Sean Kearney; Chris Forlin – Issues in Educational Research, 2024
Evidence based decision making is essential for enabling improved student learning. Teacher motivations and beliefs about the types and use of data are critical determinants of decision making. Our research explored the types of data teachers use and consider valuable when measuring improvement in student learning. Findings from 294 teachers from…
Descriptors: Catholic Schools, Elementary Secondary Education, Learning Analytics, Student Needs
Luis, Ricardo M. Meira Ferrão; Llamas-Nistal, Martin; Iglesias, Manuel J. Fernández – Smart Learning Environments, 2022
E-learning students have a tendency to get demotivated and easily dropout from online courses. Refining the learners' involvement and reducing dropout rates in these e-learning based scenarios is the main drive of this study. This study also shares the results obtained and crafts a comparison with new and emerging commercial solutions. In a…
Descriptors: Artificial Intelligence, Identification, Electronic Learning, Dropout Characteristics