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Mind the Gap: From Typical LMS Traces to Learning to Learn Journeys

Mind the Gap: From Typical LMS Traces to Learning to Learn Journeys

Carmel Kent (The Open University, UK), Abayomi Akanji (EDUCATE Ventures Research, UK), Benedict du Boulay (EDUCATE Ventures Research, UK), Ibrahim Bashir (EDUCATE Ventures Research, UK), Thomas G. Fikes (Arizona State University, USA), Sue A. Rodríguez De Jesús (Arizona State University, USA), Alysha Ramirez Hall (Arizona State University, USA), Paul Alvarado (Arizona State University, USA), Jennifer E. Jones (Arizona State University, USA), Mutlu Cukurova (University College London, UK), Varshita Sher (EDUCATE Ventures Research, UK), Canan Blake (University College London, UK), Arthur Fisher (EDUCATE Ventures Research, UK), Juliet Greenwood (Arizona State University, USA), and Rosemary Luckin (University College London, UK)
Copyright: © 2022 |Pages: 26
DOI: 10.4018/978-1-7998-9644-9.ch001
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Abstract

Many universities aim to improve students' 'learning to learn' (LTL) skills to prepare them for post-academic life. This requires evaluating LTL and integrating it into the university's curriculum and assessment regimes. Data is essential to provide evidence for the evaluation of LTL, meaning that available data sources must be connected to the types of evidence required for evaluation. This chapter describes a case study using an LTL ontology to connect the theoretical aspects of LTL with a university's existing data sources and to inform the design and application of learning analytics. The results produced by the analytics indicate that LTL can be treated as a dimension in its own right. The LTL dimension has a moderate relationship to academic performance. There is also evidence to suggest that LTL develops at an uneven pace across academic terms and that it exhibits different patterns in online as compared to face-to-face delivery methods.
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Introduction

This study was initiated to explore how, and to what degree, students at Arizona State University (ASU) acquire skills in Learning to Learn (LTL).

Helping students learn to learn is a worthwhile aim, but it needs to be actioned through explicit teaching and reflective assessment. If LTL is one of the aims of higher education, it also needs to be assessed at various stages to ensure its growth. Just as the students routinely get transcripts that reflect their performance grades, there needs to be some validation about their increasing effectiveness as learners (Molenaar et al., 2019). However, most learning management systems (LMSs) record students' actions largely as 'users' of the university rather than as learners. For example, LMSs often record students watching a video or submitting an assignment, rather than activities that offer more evidence about LTL, such as reacting to feedback (Suraworachet et al., 2021) and self-reflecting on their learning activity (Lau et al., 2017).

A Working Definition for LTL

The concept of LTL is derived from the definition of learning. Scholars continually debate the definition of learning, but most of them would agree that learning is a process, that it involves change that follows experience (Schunk, 2012), and that, for the most part, it is internal and so invisible (Lefrançois, 2019). These characteristics - all have direct consequences on how learning can be evaluated.

In this chapter, LTL was conceptualized as a process of improvement in self-regulated learning (SRL). Self-regulated learners are “meta-cognitively, motivationally, and behaviourally active participants in their own learning process” (Zimmerman,1989, p. 4).

LTL depends on each learner's ability and willingness to reflect on and thus improve their self-regulated learning capability (Education Council, 2006; Hautamäki et al., 2002). To measure this process, it is necessary to adopt a temporal viewpoint of the progress learners make in their ability to understand (and adapt) their own learning strategies, strengths, and motivation as learners (The Campaign for Learning, 2007). In the current study, the researchers operationalized the LTL journeys of university students as a temporal sequence of “snapshots”, each of which might contain some evidence about students' SRL (or SRL-related) capabilities. Examples of the types of snapshots, along with the process of suggesting which moments should be captured as snapshots are given in the ‘LTL Ontology’ section. The temporal order of these snapshots and their individual strengths of evidence about students' SRL capability were used to create an overall view of their LTL journeys.

Key Terms in this Chapter

Learning Management System (LMS): Is a software application used by educational institutions to plan, implement and assess students’ learning process.

Process Mining: Is a data science technique used to analyze and identify common process-based patterns, based on event logs.

Ontology: Is a formal, machine-readable representation of a specific body of knowledge. The ontology would typically include the definition of the relevant concepts, categories, properties and the relationships between them.

Self-Regulated Learning (SRL): Refers to students’ abilities to use their metacognition, planning, monitoring, and evaluating their own learning process.

Cluster Analysis: Is a common statistical analysis technique, focused on grouping a set of entities (e.g., students’ data) in such a way that entities clustered together are concerned as more similar to each other than to those in other clusters.

Learning to Learn (LTL): Is conceptualized as a process of improvement in the self-regulated learning (SRL) abilities of students.

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