Adaptive E-Learning Technologies for Sustained Learning Motivation in Engineering Science - Acquisition of Motivation through Self-Reports and Wearable Technology

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

“…Bauer et al [19] proposed a framework that attempts to detect learners' motivation states using wearable devices based on mobile device platform. The algorithm relies on navigation features that react based on learner's interest and competence score changes.…”

“…It is a useful approach as it supports wearable technologies and takes learners' motivation into account. However, it faces a drawback of link granularity issue and time imposition due to not considering learners' reading time [19]. Lehman and Zapata-Rivera [21] also proposed an emotional based approach that focuses on flow engagement approach of learners.…”

“…Even though for over a decade intelligent systems have adapted learners' learning styles for the specification of Learning Objects (LOs) and sustained learning content adaptation in AEHS, real-time dynamic adaptation of LOs has been a challenge due to a failure to install themselves in Learning Objects (LOs) [16,17] and unable to handle fully multimedia content [18]. Thus, navigation links granularity results into time imposition and work overload due to the granularity issue [19]. As stated earlier, most AEHS technical challenges, such as granularity-related, interoperability, reusability, or accessibility issues, are due to the challenges that restrict AEHS from obtaining accurate and useful learners' traits information, which can be used for adaptive decision-making processes [8][9][10].…”

“…Unfortunately, most of the AEHS use static basis model [17] which makes their LMS inefficient to capture accurate dynamic learners' learning styles [9]. Also, from technological perspective, the absence of proper devices for capturing physiological parameters [19] drove systems developers and researchers away from taking learners' cognitive processes, such as motivation and attention, into consideration as quantitative parameters. 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].…”

“…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].…”