Purpose: This paper defines and assesses student learning patterns under the influence of problem-based learning (PBL) and their classification into a reasonable minimum number of classes. Study utilizes PBL implemented in an undergraduate Statistics and Operations Research course for techno-management students at a private university in India.…

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.…

We propose a novel model, Wide & Deep Item Response Theory (Wide & Deep IRT), to predict the correctness of students' responses to questions using historical clickstream data. This model combines the strengths of conventional Item Response Theory (IRT) models and Wide & Deep Learning for Recommender Systems. By leveraging clickstream…

Intelligent tutoring systems (ITSs) teach skills using learning-by-doing principles and provide learners with individualized feedback and materials adapted to their level of understanding. Given a learner's history of past interactions with an ITS, a learner performance model estimates the current state of a learner's knowledge and predicts her…

This editorial introduces a special section of the "Journal of Learning Analytics," for which Neil Selwyn's keynote address to LAK '18 has been written up as an article, "What's the problem with learning analytics?" His claims and arguments are engaged in commentaries from Alfred Essa, Rebecca Ferguson, Paul Prinsloo, and…

Decades of research suggest that faculty play a critical role in student learning and success, yet at many institutions, faculty are not integrated into the institutional research function. This chapter examines how institutional research and faculty can collaborate to encourage data-informed faculty decision-making to drive student success and…

As American public education becomes increasingly technology-based, multiple new data sources become accessible for educators to better understand student learning processes. Novel data analysis applications became necessary during severe disruptions due to the COVID pandemic. This case study highlights a novel method for identifying high school…

The Responses to Educational Disruption Survey (REDS), conducted by the International Association for the Evaluation of Educational Achievement (IEA), studied how teaching and learning were affected by the disruptions caused by COVID-19 and aimed to paint a picture of national responses to the global pandemic. REDS was based on the premise that…

Bay Path University in Massachusetts developed a COVID dashboard and related safety practices enabled by data (Anderson, 2020) during the pandemic. The dashboard has capabilities, in which information technology staff partnered with executive management, human resources staff, health offices, and others to identify key performance indicators…

With the emergence of the web, traditional learning has changed significantly. Hence, a huge number of 'e-learning systems' with the advantages of time and space have been created. Currently, many e-learning systems are being used by a large number of academic institutions worldwide which allow different users of the system to perform various…

Learning analytics (LA) are a young but fast-growing field, which, according to some authors, holds big promises for education. Some claim that LA solutions can help measure and support constructivist classrooms and 21st century skills, thus creating a potential of making an alignment between LA and PBL principles and practices. Despite this…

The COVID-19 pandemic disrupted education provision at an unprecedented scale, with education systems around the world being impacted by extended school closures and abrupt changes to normal school operations. The Responses to Educational Disruption Survey (REDS) investigated how teaching and learning were affected by the health crisis, and how…

Just as accountability policies have led to increased levels of teacher data use, teachers have begun to increase the extent to which students track and analyse data about their own learning. Although some might argue that such "student-involved data use" (SIDU) might empower or motivate students to take charge of their own learning…

In this article, we propose the concept of "Autonomic Cycle Of Learning Analysis Tasks" (ACOLAT), which defines a set of tasks of learning analysis, whose objective is to improve the learning process. The data analysis has become a fundamental area for the knowledge discovery from data extracted from different sources. In the autonomic…

This study investigates a possible way to analyze chat data from collaborative learning environments using epistemic network analysis and topic modeling. A 300-topic general topic model built from TASA (Touchstone Applied Science Associates) corpus was used in this study. 300 topic scores for each of the 15,670 utterances in our chat data were…