Mobile internet, cloud computing, big data technologies, and significant breakthroughs in Artificial Intelligence (AI) have all transformed education. In recent years, there has been an emergence of more advanced AI-enabled learning systems, which are gaining traction due to their ability to deliver learning content and adapt to the individual needs of students. Yet, even though these contemporary learning systems are useful educational platforms that meet students’ needs, there is still a low number of implemented systems designed to address the concerns and problems faced by many students. Based on this perspective, a systematic mapping of the literature on AI-enabled adaptive learning systems was performed in this work. A total of 147 studies published between 2014 and 2020 were analysed. The major findings and contributions of this paper include the identification of the types of AI-enabled learning interventions used, a visualisation of the co-occurrences of authors associated with major research themes in AI-enabled learning systems and a review of common analytical methods and related techniques utilised in such learning systems. This mapping can serve as a guide for future studies on how to better design AI-enabled learning systems to solve specific learning problems and improve users’ learning experiences.

AI-enabled adaptive learning systems: A systematic mapping of the literature

Objective Competitive learning techniques are being successfully used in courses of different disciplines. However, there is still a significant gap in analyzing their effects in medical students competing individually. The authors conducted this study to assess the effectiveness of the use of a competitive learning tool on the academic achievement and satisfaction of medical students. Methods The authors collected data from a Human Immunology course in medical students (n = 285) and conducted a nonrandomized (quasi-experimental) control group pretest-posttest design. They used the Mann-Whitney U-test to measure the strength of the association between two variables and to compare the two student groups. Results The improvement and academic outcomes of the experimental group students were significantly higher than those of the control group students. The students using the competitive learning tool had better academic performance, and they were satisfied with this type of learning. The study, however, had some limitations. The authors did not make a random assignment to the control and experimental groups and the groups were not completely homogenous. Conclusion The use of competitive learning techniques motivates medical students, improves their academic outcomes and may foster the cooperation among students and provide a pleasant classroom environment. The authors are planning further studies with a more complete evaluation of cognitive learning styles or incorporating chronometry as well as team-competition.

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Effects of competitive learning tools on medical students: A case study.

Kravcik, Milos [Hrsg.]; Santos, Olga C. [Hrsg.]; Boticario, Jesus G. [Hrsg.]: PALE 2014. Personalization approaches in learning environments. Aalborg 2014, S. 55-62. - (CEUR Workshop Proceedings; 1181)
Padagogische Teildisziplin: Medienpadagogik;

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Personalized Web Learning: Merging Open Educational Resources into Adaptive Courses for Higher Education

INTUITEL is a research project aiming to offer a personalized learning environment. The INTUITEL approach includes an Intelligent Tutoring System that gives students recommendations and feedback about what the best learning path is for them according to their profile, learning progress, context and environmental influences. INTUITEL combines efficient pedagogical-based recommendations with freedom of choice and it introduces this tutoring support in different Learning Management Systems. During the INTUITEL project various software and pedagogical testing procedures were defined to provide the development teams with feedback, both summative and formative. The current paper describes the initial user test, which was conducted at the University of Valladolid for the course “Network Design”. The experiment was focused on real learners’ reactions to INTUITEL recommendations received by an INTUITEL-enabled LMS. Nineteen students participated in a two phase testing procedure in order to analyze the learners’ behavior with INTUITEL, as well as obtaining information about how learners perceive the influence and usefulness of the tutoring system in online learning courses. Results show that students with INTUITEL follow learning paths that are more suitable for them. Besides, the general satisfaction level of participants is high. Most learners appreciate INTUITEL, would follow its recommendations and consider the messages shown by INTUITEL as useful and caring.

Integration of an intelligent tutoring system in a course of computer network design

The purpose of this review is to investigate the trends in the body of research on machine learning in educational technologies, published between 2007 and 2017. The criteria for article selection ...

A review of research on machine learning in educational technology

Trahasch, Stephan [Hrsg.]; Plotzner, Rolf [Hrsg.]; Schneider, Gerhard [Hrsg.]; Gayer, Claudia [Hrsg.]; Sassiat, Daniel [Hrsg.]; Wohrle, Nicole [Hrsg.]: DeLFI 2014. Die 12. e-Learning Fachtagung Informatik der Gesellschaft fur Informatik e.V., 15.-17. September 2014 in Freiburg. Bonn : Gesellschaft fur Informatik e.V. (GI) 2014, S. 39-50. - (Lecture Notes in Informatics (LNI) - Proceedings; P-233)
Padagogische Teildisziplin: Medienpadagogik;

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Learning pathway recommendation based on a pedagogical ontology and its implementation in Moodle

This paper describes the user interface of an Intelligent Tutoring System that enables the customization of e-Learning systems according to the needs of students requiring recommendation along pedagogical pathways. The interface has been implemented for the Moodle e-learning platform by using a standard extension mechanism provided by this Learning Management System (LMS) referred as "block". The designed interface allows the intelligent system to interact with the LMS and the learner, whose learning is continuously monitored and supported by means of personalized recommendations and messages. The system usability has been evaluated in the context of a course about Computer Network Design. Results show that the designed interface is well integrated into Moodle and generates a non-intrusive user interface.

Intelligent tutoring interface for technology enhanced learning in a course of computer network design

Breiter, Andreas [Hrsg.]; Rensing, Christoph [Hrsg.]: DeLFI 2013 : die 11. E-Learning Fachtagung Informatik der Gesellschaft fur Informatik e.V., 8. - 11.9.2013 in Bremen. Bonn : GI 2013, S. 277-280. - (Lecture Notes in Informatics (LNI) - Proceedings; P-218 )
Padagogische Teildisziplin: Medienpadagogik;

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Eran Gal

Papers

Integration of an intelligent tutoring system in a course of computer network design

Learning pathway recommendation based on a pedagogical ontology and its implementation in Moodle

Intelligent tutoring interface for technology enhanced learning in a course of computer network design

Lernpfadmodellierung mit der Webdidaktik für die adaptive Erweiterung von Lernmanagementsystemen