As learning analytics use grows across U.S. colleges and universities, so does the need to discuss the plans, purposes, and paths for the data collected via learning analytics. More specifically, students, faculty, and others who are impacted by learning analytics use should have more information about their campus' learning analytics practices…

Massive open online courses (MOOCs) have become one of the most popular ways of learning in recent years due to their flexibility and convenience. However, high dropout rate has become a prominent problem that hinders the further development of MOOCs. Therefore, the prediction of student dropouts is the key to further enhance the MOOCs platform.…

Learning analytics (LA) has become a critical field for transforming educational practices through data-driven insights. This study presents a comprehensive bibliometric analysis of LA research in higher education from 2013 to 2023, utilizing the Scientific Procedures and Rationales for Systematic Literature Review (SPAR-4-SLR) approach. By…

Understanding student cognitive states is essential for assessing human learning. The deep neural networks (DNN)-inspired cognitive state prediction method improved prediction performance significantly; however, the lack of explainability with DNNs and the unitary scoring approach fail to reveal the factors influencing human learning. Identifying…

Learning analytics platforms (LAPs) have become important modes of anticipatory governance in education. Educational futures are governed by utilizing various forms of learning analytics to track student data over time, suggesting that school leaders and teachers are expected to improve school quality by engaging with digital presentations of…

We present an innovative modelling technique that simultaneously constrains student performance, course difficulty, and the sensitivity with which a course can differentiate between students by means of grades. Grade lists are the only necessary ingredient. Networks of courses will be constructed where the edges are populations of students that…

Metacognition, which involves the deliberate awareness and analysis of one's own learning and thought processes, has gained significant traction among educational researchers. The burgeoning volume of metacognition studies underscores the importance of examining its current status and evolving trends. Leveraging topic modeling and bibliometrics on…

The design of social learning analytics (SLA) tools has become a practical means to make available learning information with a goal to improve students' regulation, reflection, and engagement in online learning. This design-based research uses the multi-method analytics to iteratively design, implement, and modify the SLA tool that makes available…

Knowledge tracing (KT), the task of tracking the knowledge state of each student over time, has been assessed actively by artificial intelligence researchers. Recent reports have described that Deep-IRT, which combines Item Response Theory (IRT) with a deep learning model, provides superior performance. It can express the abilities of each student…

Technology-enhanced learning supported by virtual learning environments (VLEs) facilitates tutors and students. VLE platforms contain a wealth of information that can be used to mine insight regarding students' learning behaviour and relationships between behaviour and academic performance, as well as to model data-driven decision-making. This…

Educational data mining involves the application of data mining techniques to student activity. However, in the context of computer programming, many data mining techniques can not be applied because they require vector-shaped input, whereas computer programs have the form of syntax trees. In this paper, we present ast2vec, a neural network that…

Engineering design plays an important role in education. However, due to its open nature and complexity, providing timely support to students has been challenging using the traditional assessment methods. This study takes an initial step to employ learning analytics to build performance prediction models to help struggling students. It allows…

In order to learn the mappings from words to referents, children must integrate co-occurrence information across individually ambiguous pairs of scenes and utterances, a challenge known as cross-situational word learning. In machine learning, recent multimodal neural networks have been shown to learn meaningful visual-linguistic mappings from…

Accounting for complex interactions between contextual variables and learners' individual differences in aptitudes and background requires building the means to connect and access learner data at large scales, across time, and in multiple contexts. This paper describes the ASU Learning@Scale (L@S) project to develop a digital learning network…

This paper addresses dynamical interdependence among the actions of group members. I assume that the actions of each member can be represented as nodes of a dynamical network and then collect the nodes into disjoint subsets (components) representing the individual group members. Interdependence among group members' actions can then be defined with…