Learning analytics for IoE based educational model using deep learning techniques: architecture, challenges and applications

Abstract

The new generation teaching-learning pedagogy has created a complete paradigm shift wherein the teaching is no longer confined to giving the content knowledge, rather it fosters the "how, when and why" of applying this knowledge in real world scenarios. By exploiting the advantages of deep learning technology, this pedagogy can be further fine-tuned to develop a repertoire of teaching strategies. This paper presents a secured and agile architecture of an Internet of Everything (IoE) based Educational Model and a Learning Analytics System (LAS) model using the concept of deep learning which can be used to gauge the degree of learning, retention and achievements of the learners and suggests improvements and corrective measures. The paper also puts forward the advantages, applications and challenges of using deep learning techniques for gaining insights from the data generated from the IoE devices within the educational domain for creating such learning analytics systems. Finally a feature wise comparison is provided between the proposed Learning Analytics (LA) based approach and conventional teaching-learning approach in terms of performance parameters like cognition, attention, retention and attainment of learners.

DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era

“…From a more general perspective, DeepLMS aligns with the previous efforts that incorporate LSTM-based predictions in the context of online education, yet not at the exact same specific problem settings as in DeepLMS. Hence, the latter is well-positioned with the approaches related to: i) cross-domains analysis, e.g., MOOCs impact in different contexts 57 , as DeepLMS could be easily adapted to a micro analysis of the QoI per discipline/course and transfer learning from one discipline to another at the same course (or courses with comparable content), as shown here with the application of DeepLMS to DB1-DB3, in a similar manner that was applied in MOOCs from different domains 57 ; ii) combination of learning patterns in the context domain with the temporal nature of the clickstream data 58 , and identification of students at risk 59 , as DeepLMS could be combined with an autoencoder to capture both the underlying behavioral patterns and the temporal nature of the interaction data at various levels of the predicted QoI (e.g., low (<0.5) QoI (at risk level)); iii) predicting learning gains by incorporating skills discovery 60,61 , as DeepLMS could provide the predicted QoI as an additional source of the user profile to his/her skills and learning gains; iv) user learning states and learning activities prediction from wearable devices 62 , as DeepLMS could easily be embedded in the expanded space of affective (a-) learning, and inform a more extended predictive model that would incorporate the learning state with the estimated QoI; v) increasing the communication of the instructional staff to learners based on individual predictions of their engagement during MOOCs 63,64 , as DeepLMS could facilitate the coordination of the instructor with the learner based on the informed predicted QoI; and vi) predicting the learning paths/performance 65 and the teaching paths 66 , as the DeepLMS could be extended in the context of affecting the learning/teaching path by the predicted QoI.…”

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

“…Several works have looked at the design of the applications for smart societies. Ahad et al [ 77 ] have developed a smart educational environment based on IoE to produce a learning analytics system that evaluates the learning process and achievements. Al-dhubhani et al [ 78 ] have proposed a smart border security system where sensors and different sources of data are used to make decisions and take actions.…”

Student Participation in Computing Studies to Understand Engagement and Grade Outcome

“…More recent methods for LA include machine learning (ML) (Sharma et al, 2019) and deep learning (DL) (Ahad et al, 2018). Such methods are well suited for the discovery of insights embedded within large and/or diverse data sets, or to build predictive models of student outcomes such as whether they are at risk of failing a subject (Akçapınar et al, 2019).…”

“…Akçapınar et al (2019) built a predictive classifier that could by week 3 accurately classify 20 out of 27 students that would fail the subject. Ahad et al (2018) considered the "how, when and why" learning was taking place through the proposal of a framework that utilises DL to analyse highly diverse data collected through an installation of Internet of Everything (IoE) infrastructure at learning institutions. Sensors worn by students can collect activity movements and patterns, location tracking, and class attendance, while sensors installed at learning locations (e.g., classrooms and laboratories) can track environmental metrics such as temperature, light sources, and humidity.…”