Dissecting learning tactics in MOOC using ordered network analysis

Abstract

Background: Select and enact appropriate learning tactics that advance learning has been considered a critical set of skills to successfully complete highly flexible online courses, such as Massive open online courses (MOOCs). However, limited by analytic methods that have been used in the past, such as frequency distribution, sequence mining and process mining, we lack a deep, complete and detailed understanding of the learning tactics used by MOOC learners.Objectives: In the present study, we proposed four major dimensions to better interpret and understand learning tactics, which are frequency, continuity, sequentiality and role of learning actions within tactics. The aim of this study was to examine to what extent can a new analytic technique, the ordered network analysis (ONA), deepen the understanding of MOOC learning tactics compared to using other methods. Methods:In particular, we performed a fine-grained analysis of learning tactics detected from more than 4 million learning events in the behavioural trace data of 8788 learners who participated in a large-scale MOOC 'Flipped Classroom'. Results and Conclusions:We detected eight learning tactics, and then chose one typical tactic as an example to demonstrate how the ONA technique revealed all four dimensions and provided deeper insights into this MOOC learning tactic. Most importantly, based on the comparison with different methods such as process mining, we found that the ONA method provided a unique opportunity and novel insight into the roles of different learning actions in tactics which was neglected in the past.Takeaway: In summary, we conclude that ONA is a promising technique that can benefit the research on learning tactics, and ultimately benefit MOOC learners by strengthening the strategic support.

Revealing Networks: Understanding Effective Teacher Practices in AI-Supported Classrooms using Transmodal Ordered Network Analysis

“…Due to the unique affordances of unifying descriptions and quantitative representation, QE has a rising interest in learning analytics. For example, Epistemic Network Analysis models behaviors captured in learning environments connected via temporal co-occurrence, which supports interpretations of learner strategy differences and their relationship to outcomes [12,15,48]. For example, Fougt et al [15] distinguish student proficiency levels in higher education writing assessment to support grading via keywords.…”

Towards a fuller picture: Triangulation and integration of the measurement of self‐regulated learning based on trace and think aloud data

“…To build a reliable and valid process library, we had initially harnessed the SRL processes based on Bannert's SRL coding protocol (Bannert, 2007), and then included additional relevant processes from several related studies (e.g., Fan et al., 2021; Fan, Lim et al 2022; Kizilcec et al, 2017; Saint et al, 2021, 2020; Siadaty et al, 2016). As a result, we developed the process library shown in Table 3.…”

“…For example, based on MOOC learners' navigational logs, Matcha et al (2019) detected different learning tactics and learning strategies by identifying clusters of similar SRL behaviours of learners; Rakovic et al (2022) unobtrusively measured metacognitive processes using learners' trace data during a multi‐source writing task (e.g., mouse clicks and keyboard strokes) and used features extracted from these trace data to investigate how SRL processes are associated with the quality of a written product; Fan et al. (2022) used eye‐tracking, mouse movement, and keystroke data as additional data channels to improve the recording of learning actions (e.g., learner's interactions with certain learning tools) and detect SRL processes (e.g., monitoring and evaluating processes during learning) while enhancing the granularity of the measurement; Fan et al. (2022) concluded that eye‐tracking data is particularly valuable for measuring and extracting SRL processes.…”

“…(2022) used eye‐tracking, mouse movement, and keystroke data as additional data channels to improve the recording of learning actions (e.g., learner's interactions with certain learning tools) and detect SRL processes (e.g., monitoring and evaluating processes during learning) while enhancing the granularity of the measurement; Fan et al. (2022) concluded that eye‐tracking data is particularly valuable for measuring and extracting SRL processes.…”

Investigating the reliability of aggregate measurements of learning process data: From theory to practice

“…Co‐temporal characteristics refer to multiple events co‐occurring within a window, regardless of in which order (Hadwin, 2021). In contrast, sequential characteristics, also named temporal order, are concerned with the order of multiple events in time (Chen et al, 2018; Fan, Tan, et al, 2022). Examples are patterns of ordered actions that frequently occur during an educational game.…”