Utilization of Learners’ Metacognitive Experiences to Monitor Learners’ Cognition States in e-Learning Platforms

Abstract

Optimal learning environment highly depends on aptitude treatment interaction. Even though ICT (Information and Communication Technologies) advancement supports knowledge sharing platforms including e-learning and multimedia, designing of learning content in such platforms relies on the aptitude treatment interaction to sustain a learner-centric environment. However, achieving the optimal learning environment in e-learning platform is still a challenge due to variation of learner's skills and abilities to both learning process and learning content. This makes difficulties in monitoring learner's cognition states and quality of learning content. To overcome the difficulties, in this study, a learner-centric metacognitive experiences based approach has been proposed (e-learning Prior Knowledge Assessment System-ePKAS), ePKAS is supporting the detection and evaluation of learners' prior knowledge profiles in turn to enable monitoring of learners' cognition states and adaptation of cognitive states in to e-learning platforms based on visual contact. The study has investigated students' reactions to multimedia content based on their past experiences. The results show that students respond more attentively and accurately (93%) to the learning content that is closely related to their past experiences. Scope of this study based on visual contact in order to support the involvement of people with hearing impairment in e-learning platforms.

Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-learning Content Personalization

“…The cognitive process referred to in this study is learner's intrinsic motivation states during the learning process. With respect to this study hypothesis described in Section 1, learner's motivation state alters when experiencing work overload (difficult learning) [22,26,27]. Hence, this study approach utilizes such alteration of learner's motivation states to initiate navigation support process which supports AEHS adaptive decision-making process.…”

“…The approach is still at preliminary stage but paves a way for the future studies on AEHS to use Eye Tracking for physiopsycho data acquisition [18]. Mwambe, Othmar, and Kamioka [22] proposed a Brain-Computer Interface (BCI)-based approach namely e-learning Prior Knowledge Assessment System (ePKAS) that can detect learners' prior knowledge profiles level and their matching score with multimedia content [22]. ePKAS algorithm relies on learners' biological information that was acquired using EEG sensor and learners' brain waves activation was used to recommend learners' knowledge profiles for e-learning management system (LMS) in supporting people with hearing impairment (as shown in Figure 2).…”

“…Thus, almost all the existing AEHS are formulated merely on the basis of two common pedagogical learning theories, namely constructivism and cognitivism, which do not fully support such learners' cognitive processes [8][9][10]. However, very few recent studies on AEHS have started exploring other pedagogical learning theories so as to adapt learners' cognitive processes [18][19][20][21] and metacognitive skills [22] into e-learning platforms. Such cognitive processes play a crucial role in predicting learners' performance, attention level [23,24], and cognitive load [25][26][27].…”

“…Such cognitive processes play a crucial role in predicting learners' performance, attention level [23,24], and cognitive load [25][26][27]. Hence, the design of this study approach is highly influenced by the previously mentioned pilot studies on Brain-Computer Interface (BCI) [23][24][25][26] and cognitive load [22,27]. Thus, this study approach derives its conceptual framework not only from educational technology (AEHS) [28] but also cognitive science (visual spatial attention and cognitive load) and educational psychology.…”

“…The purpose of this study is to reduce time imposed by navigation links and work overload so as to increase learners' motivation by supporting AEHS adaptive-decision process. To achieve such objective, this study proposes a bioinformatics-based adaptive navigation support as an extension of our previous studies on utilization of learners' metacognitive experiences [22,26]. This study hypothesizes that as long as metacognitive experiences influence learners' cognitive processes and learning styles during the learning process, then such cognitive states' alteration can be utilized to support real-time integration of learners' dynamic learning styles (LS) with LO models in AEHS and obtain timely accurate information for an adaptive decision-making process.…”