An ontology‐based framework of assessment analytics for massive learning

Abstract

Analyzing learning traces is presently highly required in e‐learning environment. Several communities have been developed to address this need, such as those of Learning Analytics and Educational Data Mining. The main step of performing a learning analytics process is the educational data collection. Actually, learning environments such as Massive Open Online Course (MOOC) generate a big amount of educational data. They can be divided into assessment data, collaboration data, communication data, and so on. When we focus on assessment, we can launch a new source of data that can be analyzed and hence contribute to the improvement of learning analytics field. In this paper, we explore, investigate and compare the set of learning analytics models in the literature. Then, we study them from assessment point of view. The only current learning analytics model which can support tracking and modeling assessment data is the xAPI data model. For this reason, we study and investigate the xAPI specification from assessment point of view. Based on identified weaknesses of xAPI specification, we propose an enhancement of its data model. This is to support the assessment analytics effectively. We present an ontological model for assessment analytics inspired from the xAPI specification. To validate our approach, we focus on massive learning traces extracted from a real MOOC. Thus, we define and execute the set of proposed steps of preprocessing stage that extracts assessment data from whole learning data. Furthermore, we develop a java semantic web application to convert assessment data extracted to OWL file according to our proposed ontological model for assessment analytics.

Utilising learning analytics to support study success in higher education: a systematic review

“…To date, the study of learning analytics has tended to evolve exponentially from the early 2010s in the areas of education and psychology, as well as computing and data science (Prieto et al 2019). As a result, while the concept of learning analytics is vaguely defined, it sees a plethora of conceptual variations, including school analytics (Sergis and Sampson 2016), teacher or teaching analytics (Sergis and Sampson 2017), academic analytics (Long and Siemens 2011), assessment analytics (Nouira et al 2019), social learning analytics (Buckingham Shum and Ferguson 2012), or multimodal learning analytics (Blikstein and Worsley 2016). For this systematic literature review with a specific focus on learning analytics in higher education and its link to study success, learning analytics are defined as "the use, assessment, elicitation and analysis of static and dynamic information about learners and learning environments, for the near real-time modelling, prediction and optimisation of learning processes, and learning environments, as well as for educational decision-making" (Ifenthaler 2015, p. 447).…”

Improving the portability of predicting students’ performance models by using ontologies

“…In Grivokostopoulou et al (2014) they propose an educational system that utilizes ontologies and semantic rules to enhance the quality of educational content (curriculum) and the learning activities delivered to each student. In Nouira et al (2019), they propose an ontological model for assessment analytics. And finally; in Dorça et al (2017), they present an approach for the automatic and dynamic analysis of learning object repositories in which ontology models the relationships between the attributes and learning styles of the learning objects.…”

Supporting teaching staff through data Analytics: A Systematic Review

“…For instance, Wong (2017) presents several case studies utilising learning analytics for (a) improving student retention, (b) supporting informed decision making, (c) increasing cost-effectiveness, (d) helping to understand learning behaviour, (e) providing personalised assistance, and (f) delivering feedback and interventions. More recently, the field of learning analytics established conceptual variations, including school analytics (Sergis & Sampson, 2016), academic analytics (Long & Siemens, 2011), assessment analytics (Nouira et al, 2019), social learning analytics (Gašević et al, 2019), multimodal learning analytics (Blikstein & Worsley, 2016), and teacher or teaching analytics (Sergis & Sampson, 2017).…”