Intelligent Agents and Game-Based Learning Modules in a Learning Management System

Kuk, Kristijan; Rančić, Dejan; Pronić-Rančić, Olivera; Ranđelović, Dragan

doi:10.1007/978-3-319-39883-9_19

Cited by 6 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Software agents can be defined as programs that act on behalf of human beings, resolving inconsistencies, finding and integrating information from different sources, filtering out irrelevant or unwanted information, and adapting them to human needs [7]. Pedagogical agents are autonomous software entities that support the learning process through interaction with students, lecturers, and other participants and in collaboration with other similar agents [12]. The Observer system uses the following: tutoring agents; an agent who collects data, stores them in the system's internal database, and informs tutoring agents about student suggestions from the system; a data preparation agent; and an agent who learns from the data and forwards the results (as shown in Figure 1).…”

Section: Observer Its Systemmentioning

confidence: 99%

Multi-Agent System Observer: Intelligent Support for Engaged E-Learning

Vuković

Kuk²,

Čisar³

et al. 2021

Electronics

Self Cite

View full text Add to dashboard Cite

Moodle is a widely deployed distance learning platform that provides numerous opportunities to enhance the learning process. Moodle’s importance in maintaining the continuity of education in states of emergency and other circumstances has been particularly demonstrated in the context of the COVID-19 virus’ rapid spread. However, there is a problem with personalizing the learning and monitoring of students’ work. There is room for upgrading the system by applying data mining and different machine-learning methods. The multi-agent Observer system proposed in our paper supports students engaged in learning by monitoring their work and making suggestions based on the prediction of their final course success, using indicators of engagement and machine-learning algorithms. A novelty is that Observer collects data independently of the Moodle database, autonomously creates a training set, and learns from gathered data. Since the data are anonymized, researchers and lecturers can freely use them for purposes broader than that specified for Observer. The paper shows how the methodology, technologies, and techniques used in Observer provide an autonomous system of personalized assistance for students within Moodle platforms.

show abstract

Section: Observer Its Systemmentioning

confidence: 99%

Multi-Agent System Observer: Intelligent Support for Engaged E-Learning

Vuković

Kuk²,

Čisar³

et al. 2021

Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many authors note the need for a balance between subject matter with gameplay (for example, Kuk, Rančić, Pronić-Rančić, & Ranđelović, 2016). Laamarti, Mohamad, & Saddik (2014) do represent the question of fun and the main purpose of the game as the main source of success for a serious game.…”

Section: Problem Statementmentioning

confidence: 99%

Classification Of Educational Games According To Their Complexity And The Player's Skills

Bylieva¹

2018

The European Proceedings of Social and Behavioural Sciences

View full text Add to dashboard Cite

show abstract

“…The possibilities of educational games are, however, far from being exhausted by these goals. Kuk, Rančić, Pronić-Rančić, & Ranđelović (2016) note that the games in the educational process are structured in such a way as to leverage specific objectives in connection with learning objectives (LO). The ability to estimate a person's game activity in accordance with numerous criteria for evaluation allows you to use games as an analogue of testing, including both the assessment of knowledge, skills, and personality traits.…”

Section: Introductionmentioning

confidence: 99%