The effect of emotional analysis on the improvement of experimental e‐learning systems

Khalfallah, Jihen; Slama, Jaleleddine Ben Hadj

doi:10.1002/cae.22075

Cited by 10 publications

(5 citation statements)

References 41 publications

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“…This ITS is able to monitor the evolution of the learning process as the student interacts with the system. In another study, Khalfallah and Slama (2018) built an ITS called LabTutor for engineering students. LabTutor served as an experienced instructor in enabling students to access and perform experiments on laboratory equipment while adapting to the profile of each student.…”

Section: Intelligent Tutoring Systemmentioning

confidence: 99%

Artificial intelligence in higher education: the state of the field

Crompton

Burke²

2023

Int J Educ Technol High Educ

325

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This systematic review provides unique findings with an up-to-date examination of artificial intelligence (AI) in higher education (HE) from 2016 to 2022. Using PRISMA principles and protocol, 138 articles were identified for a full examination. Using a priori, and grounded coding, the data from the 138 articles were extracted, analyzed, and coded. The findings of this study show that in 2021 and 2022, publications rose nearly two to three times the number of previous years. With this rapid rise in the number of AIEd HE publications, new trends have emerged. The findings show that research was conducted in six of the seven continents of the world. The trend has shifted from the US to China leading in the number of publications. Another new trend is in the researcher affiliation as prior studies showed a lack of researchers from departments of education. This has now changed to be the most dominant department. Undergraduate students were the most studied students at 72%. Similar to the findings of other studies, language learning was the most common subject domain. This included writing, reading, and vocabulary acquisition. In examination of who the AIEd was intended for 72% of the studies focused on students, 17% instructors, and 11% managers. In answering the overarching question of how AIEd was used in HE, grounded coding was used. Five usage codes emerged from the data: (1) Assessment/Evaluation, (2) Predicting, (3) AI Assistant, (4) Intelligent Tutoring System (ITS), and (5) Managing Student Learning. This systematic review revealed gaps in the literature to be used as a springboard for future researchers, including new tools, such as Chat GPT.

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Section: Intelligent Tutoring Systemmentioning

confidence: 99%

Artificial intelligence in higher education: the state of the field

Crompton

Burke²

2023

Int J Educ Technol High Educ

325

View full text Add to dashboard Cite

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“…in courses or in lab sessions [63,68]. In discussing artificial intelligence implementations, Khalfallah and Slama [37] mention an intelligent tutoring system called "LabTutor," which they adapted to the online profile of every student to serve as an experienced teacher for students who access and perform experiments on real laboratory equipment. Through F I G U R E 4 Geographical presentation of engineering through distance education publications technology-enhanced learning or distance learning practices, students can have the opportunity to collaborate in virtual communities.…”

Section: Text Mining and Social Network Analysismentioning

confidence: 99%

“…Similarly, the fully online engineering courses could use a combination of virtual or remote laboratories, residential schools, on-campus modules, workplace experience, and simulation software [59]. The study conducted by Khalfallah and Slama [37] revealed that a majority of students realized that remote practical work can be extremely useful for engineering students.…”

Section: Text Mining and Social Network Analysismentioning

confidence: 99%

Engineering through distance education in the time of the fourth industrial revolution: Reflections from three decades of peer reviewed studies

Kocdar

Bozkurt

Doğan

2020

Comp Applic In Engineering

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In this current era of the fourth industrial revolution, the fifth generation of distance education has held out hopes of unique opportunities for engineering. With the aim of investigating the field of engineering through distance education, this study conducted a systematic review involving text-mining and social network analysis to examine 120 publications on this topic. The time trend analysis found that there has been a steadily increasing interest in engineering through distance education. Analysis of subject areas revealed that a considerable amount of the publications on engineering through distance education falls under the social sciences subject area, suggesting that there is a need for more interdisciplinary studies. In examining the geographical distribution of the publications, it was observed that most contributions come from the United States, a developed country that has invested heavily in educating students through distance education. The research patterns identified through text-mining and social network analysis revealed three major themes: (I) technology-enhanced distance engineering education, (II) e-learning and m-learning, and (III) virtual and remote labs.

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“…"Emotions are basic psychological systems regulating an individual's adaptation to personal and environmental demands. Emotions are closely related to cognitive, behavioral, motivational and physiological processes; therefore they are generally important for learning and achievement" (Seel, 2012;Khalfallah and Slama, 2018). An influential research on human behaviour put forward that the learning process is more effective when it is associated with positive relations than it is with building negative one.…”

Section: Emotionsmentioning

confidence: 99%

Emotion-Based Adaptive Learning Systems

Taurah¹,

Bhoyedhur²,

Sungkur³

2020

Lecture Notes in Computer Science

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Right from our primary school to professional academic level, the classical education system modus operandi, forces us to follow a series of predefined steps to climb the stairs of academic levels. Traditionally those predefined steps forces students to go through the beginner level to advanced level and then specialized in a specific level. The main problem was that the teaching styles and content delivery was not tailored to every learning styles and student personalities. The traditional education system is moving towards adaptive learning system where students are not bound only to one predefined set of contents. Therefore the traditional "one size fits all" approach is no longer valid as it were before. Each student has their curriculum based on their unique needs and personality. Adaptive learning may be referred as the process of creating unique learning experience for each and every learner based upon the learner's personality, interests and performance. This research presents a novel approach of adaptive learning by presenting an emotion-based adaptive learning system where the emotion and psychological traits of the learner is considered to provide learning materials that would be most appropriate at that particular instance of time. It shall demonstrate an intelligent agent based expert system using artificial intelligence and emotion detections capabilities to measure the user learning rate and find an optimum learning scheme for the latter.

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The effect of emotional analysis on the improvement of experimental e‐learning systems

Cited by 10 publications

References 41 publications

Artificial intelligence in higher education: the state of the field

Artificial intelligence in higher education: the state of the field

Engineering through distance education in the time of the fourth industrial revolution: Reflections from three decades of peer reviewed studies

Emotion-Based Adaptive Learning Systems

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