Identification of personal traits in adaptive learning environment: Systematic literature review

Normadhi1, Nur Baiti Afini; Shuib, Liyana; Nasir, Hairul Nizam Md; Bimba, Andrew Thomas; Idris, Norisma; Balakrishnan, Vimala

doi:10.1016/j.compedu.2018.11.005

Cited by 146 publications

(130 citation statements)

References 40 publications

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“…Self-assessment leads students to a greater self-awareness, by facilitating self-regulation of motivation and actions (McMillan & Hearn, 2008), and inherently promotes students' effortful behavior, because the result of the assessment is primarily important to the student herself (Papamitsiou & Economides, 2019). Furthermore, adaptation has shown a positive impact on learners' engagement with the activities (Normadhi et al, 2019), which is also reflected on learners' performance (Barla et al, 2010;Liu, McKelroy, Corliss, & Carrigan, 2017). In addition, previous work with multimodal data yielded results in other learning settings.…”

Section: Motivation Of the Research And Research Questionmentioning

confidence: 99%

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Sharma¹,

Papamitsiou²,

Giannakos³

2019

Brit J Educational Tech

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Students' on‐task engagement during adaptive learning activities has a significant effect on their performance, and at the same time, how these activities influence students' behavior is reflected in their effort exertion. Capturing and explaining effortful (or effortless) behavior and aligning it with learning performance within contemporary adaptive learning environments, holds the promise to timely provide proactive and actionable feedback to students. Using sophisticated machine learning (ML) algorithms and rich learner data, facilitates inference‐making about several behavioral aspects (including effortful behavior) and about predicting learning performance, in any learning context. Researchers have been using ML methods in a “black‐box” approach, ie, as a tool where the input data is the learner data and the output is a given class from the chosen construct. This work proposes a methodological shift from the “black‐box” approach to a “grey‐box” approach that bridges the hypothesis/literature‐driven (feature extraction) “white‐box” approach with the computation/data‐driven (feature fusion) “black‐box” approach. This will allow us to utilize data features that are educationally and contextually meaningful. This paper aims to extend current methodological paradigms, and puts into practice the proposed approach in an adaptive self‐assessment case study taking advantage of new, cutting‐edge, interdisciplinary work on building pipelines for educational data, using innovative tools and techniques. What is already known about this topic Capturing and measuring learners' engagement and behavior using physiological data has been explored during the last years and exhibits great potential. Effortless behavioral patterns commonly exhibited by learners, such as “cheating,” “guessing” or “gaming the system” counterfeit the learning outcome. Multimodal data can accurately predict learning engagement, performance and processes. What this paper adds Generalizes a methodology for building machine learning pipelines for multimodal educational data, using a modularized approach, namely the “grey‐box” approach. Showcases that fusion of eye‐tracking, facial expressions and arousal data provide the best prediction of effort and performance in adaptive learning settings. Highlights the importance of fusing data from different channels to obtain the most suited combinations from the different multimodal data streams, to predict and explain effort and performance in terms of pervasiveness, mobility and ubiquity. Implications for practice and/or policy Learning analytics researchers shall be able to use an innovative methodological approach, namely the “grey‐box,” to build machine learning pipelines from multimodal data, taking advantage of artificial intelligence capabilities in any educational context. Learning design professionals shall have the opportunity to fuse specific features of the multimodal data to drive the interpretation of learning outcomes in terms of physiological learner states. The constraints from th...

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Section: Motivation Of the Research And Research Questionmentioning

confidence: 99%

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Sharma¹,

Papamitsiou²,

Giannakos³

2019

Brit J Educational Tech

View full text Add to dashboard Cite

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“…According to Truong [7] and Normadhi [3], researchers have been searching for mechanisms to automatically detect student's learning styles based on different models. The process of automatic learning style detection can be divided into three subproblems: (a) select a suitable learning model, (b) select the descriptors and targets to represent a student's online behavior (in a MOOC), and (c) select the algorithm (and hyperparameters) which fit to the multi-target prediction issue.…”

Section: Related Work and Conceptsmentioning

confidence: 99%

“…Various components of learning styles have been researched, both conceptually and empirically [3]. In addition, numerous hypotheses and multiple taxonomies attempting to describe how people reason and learn have been proposed, arranging individuals into distinct groups.…”

Section: Learning Styles Model Selectionmentioning

confidence: 99%

“…From FSLSM theory, there are four dimensions that describe learning styles: processing, perception, input, and understanding. According to Truong [7] and Normadhi [3], many researchers developed automatic detection models based on the FSLSM, whose four dimensions are directly derived from its four objectives-processing, perception, input, and understanding. The processing dimension characterizes the active and reflective learners which are identified by their interest in performing physical or theoretical activities.…”

Section: Learning Styles Model Selectionmentioning

confidence: 99%

“…In another survey paper, Normadhi et al [3] stated that the techniques used to recognize personality characteristics can be divided into three categories: (a) questionnaires, (b) computer-based detection, and (c) both. Computer-based identification strategies are most often used to improve obtaining personality trait data in a student profile by analyzing implicit user input.…”

Section: Related Workmentioning

confidence: 99%

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Featured Application: Our results can be applied to identifying of students' learning style providing adaptation to e-learning systems.Abstract: It is possible to classify students according to the manner they recognize, process, and store information. This classification should be considered when developing adaptive e-learning systems. It also creates a comprehension of the different styles students demonstrate while in the process of learning, which can help adaptive e-learning systems offer advice and instructions to students, teachers, administrators, and parents in order to optimize students' learning processes. Moreover, e-learning systems using computational and statistical algorithms to analyze students' learning may offer the opportunity to complement traditional learning evaluation methods with new ones based on analytical intelligence. In this work, we propose a method based on deep multi-target prediction algorithm using Felder-Silverman learning styles model to improve students' learning evaluation using feature selection, learning styles models, and multiple target classification. As a result, we present a set of features and a model based on an artificial neural network to investigate the possibility of improving the accuracy of automatic learning styles identification. The obtained results show that learning styles allow adaptive e-learning systems to improve the learning processes of students.

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Emergency response (ER) workers perform extremely demanding physical and cognitive tasks that can result in serious injuries and loss of life. Human augmentation technologies have the potential to enhance physical and cognitive work‐capacities, thereby dramatically transforming the landscape of ER work, reducing injury risk, improving ER, as well as helping attract and retain skilled ER workers. This opportunity has been significantly hindered by the lack of high‐quality training for ER workers that effectively integrates innovative and intelligent augmentation solutions. Hence, new ER learning environments are needed that are adaptive, affordable, accessible, and continually available for reskilling the ER workforce as technological capabilities continue to improve. This article presents the research considerations in the design and integration of use‐inspired exoskeletons and augmented reality technologies in ER processes and the identification of unique cognitive and motor learning needs of each of these technologies in context‐independent and ER‐relevant scenarios. We propose a human‐centered artificial intelligence (AI) enabled training framework for these technologies in ER. Finally, how these human‐centered training requirements for nascent technologies are integrated in an intelligent tutoring system that delivers across tiered access levels, covering the range of virtual, to mixed, to physical reality environments, is discussed.

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Identification of personal traits in adaptive learning environment: Systematic literature review

Cited by 146 publications

References 40 publications

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Use of Deep Multi-Target Prediction to Identify Learning Styles

Human‐centered intelligent training for emergency responders

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