An Analytics-Based Approach to Managing Cognitive Load by Using Log Data of Learning Management Systems and Footprints of Social Media

By Yen, Cheng-Huang; Chen, I-Chuan et al. | Educational Technology & Society, October 2015 | Go to article overview

An Analytics-Based Approach to Managing Cognitive Load by Using Log Data of Learning Management Systems and Footprints of Social Media


Yen, Cheng-Huang, Chen, I-Chuan, Lai, Su-Chun, Chuang, Yea-Ru, Educational Technology & Society


Introduction

The cognitive processing capacity of human memory is limited, as discussed in the literature on limited capacity assumptions (Mayer, 2005). Students participating in online learning experience changes in their cognitive load over time. Instructional activities as well as the topics discussed on forums and the interaction during synchronous video conferences contribute to the cognitive load of online students. Cognitive load varies dynamically with the learning process. Paas and van Merrienboer (1994) proposed an instructional design model for providing instructional strategies for controlling cognitive load during training in complex cognitive tasks. Mental-effort-based measures were recommended as suitable tools for investigating and determining the cognitive load of instructional manipulations. In our research, an analytics-based approach was proposed for measuring and managing cognitive load. Measurements of cognitive load were associated with learning behaviors that were captured by learning management system (LMS) log data and social media footprints.

LMSs enable teachers to monitor online student learning through system-generated reports. Teachers may query the system to determine the amount of time that their students have spent browsing course content or the number of discussion forum posts to which they have contributed. The size and value of the log data from LMSs can be called a type of big data, as discussed by Snijders, Matzat, and Reips (2012). Theories and technologies have been developed to mine actionable information from big data in various application areas. In this research, we explored the possibility of using educational platform log data to manage the cognitive load of learners.

Managing cognitive load by using knowledge of learning behaviors and analytics computing

Cognitive Load Theory (CLT) is a psychological learning theory that has provided a basis for exploring instructional design and learning processes with human cognitive architecture (Sweller, van Merrienboer, & Paas, 1998; Sweller, Ayres, & Kalyuga, 2011). Three sources of cognitive load were identified in CLT: intrinsic cognitive load (ICL), extraneous cognitive load (ECL), and germane cognitive load (GCL) (Sweller et al., 1998). ICL is determined by the inherent nature of the learning material and learners' prior knowledge. ICL cannot be changed by instructional interventions. ECL is determined by instructional design, including how instructional content is designed and the activities required of learners. Sweller et al. (1998) described GCL as the effort involved in relating prior knowledge to current instructional content in order to construct schemas stored in the long-term memory. The three types of cognitive load are additive and contribute to a learner's global cognitive load that cannot exceed his or her cognitive resources. Otherwise the learning process will fail. On the other hand, learning is ineffective with no or low cognitive load. Although high ICL and ECL is detrimental to the learning process, GCL can help learners to construct schemas that are stored in long-term memory and can be retrieved later in learning or problem-solving. According to CLT, the aim of the instructional design is to reduce ICL and ECL, and to induce GCL while keeping the global cognitive load under the limit of learners' cognitive resources.

The level of ICL for a learning task is determined by the level of element interactivity and learner's prior knowledge (Sweller, 2010). An element can be a concept or a procedure that needs to be learned. Low element interactivity materials allow individual elements to be learned with minimal reference to other elements and impose a low working memory load. The concept of element interactivity can be used to explain why some material is difficult to learn or understand (Sweller, 1994).

Figure 1 illustrates the relationships among learning behaviors, log data, and students in a technology-based learning environment. …

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