Search

In intelligent e-learning systems that adapt a learning and teaching process to student knowledge, it is important to adapt the system as quickly as possible. However, adaptation is not possible until the student model is initialized. In this paper, a new approach to student model initialization using domain knowledge representative subset is described. The approach defines which concepts from domain knowledge should be included in the initial test so the system can make conclusions about what students truly know about domain knowledge. This representative subset of domain knowledge is defined using non-semantic mathematical approach based on graph theory. The initial test, created over a domain knowledge representative subset, guarantees encompassing all concepts that are relevant to domain knowledge. A two-level case study is conducted on what would be the representative subset of one selected domain knowledge. It compares semantically selected domain knowledge representative subsets (semantical analysis was done by domain area experts) to a non-semantical, mathematically selected domain knowledge representative subset. The results of the case study show that problems of inequality of semantically selected domain knowledge representative subsets are easily overcome using the presented approach.

The idea of clustering students according to their online learning behavior has the potential of providing more adaptive scaffolding by the intelligent tutoring system itself or by a human teacher. With the aim of identifying student groups who would benefit from the same intervention in AC-ware Tutor, this research examined online learning behavior using 8 tracking variables: the total number of content pages seen in the learning process; the total number of concepts; the total online score; the total time spent online; the total number of logins; the stereotype after the initial test, the final stereotype, and the mean stereotype variability. The previous measures were used in a four-step analysis that consisted of data preprocessing, dimensionality reduction, the clustering, and the analysis of a posttest performance on a content proficiency exam. The results were also used to construct the decision tree in order to get a human-readable description of student clusters.

Probabilistic student model based on Bayesian network enables making conclusions about the state of student’s knowledge and further learning and teaching process depends on these conclusions. To implement the Bayesian network into a student model, it is necessary to determine “a priori” probability of the root nodes, as well as, the conditional probabilities of all other nodes. In our approach, we enable non-empirical mathematical determination of conditional probabilities, while “a priory” probabilities are empirically determined based on the knowledge test results. The concepts that are believed to have been learned or not learned represent the evidence. Based on the evidence, it is concluded which concepts need to be re-learned, and which not. The study described in this paper has examined 15 ontologically based Bayesian student models. In each model, special attention has been devoted to defining “a priori” probabilities, conditional probabilities and the way the evidences are set in order to test the successfulness of student knowledge prediction. Finally, the obtained results are analyzed and the guidelines for ontology based Bayesian student model design are presented.

U radu su prikazani rezultati istraživanja o učinku učenja i poučavanja učenika sedmih razreda osnovne škole pomoću sustava „Moodle“ za područno znanje nastavnog predmeta hrvatski jezik. Istraživanje su provele učiteljice hrvatskoga jezika i studentice druge godine diplomskog studija informatike Prirodoslovno-matematičkog fakulteta Sveučilišta u Splitu iz kolegija „Vrednovanje sustava e-učenja“ u zimskom semestru akademske godine 2011./2012. Obuhvaćen je uzorak od dvaju razrednih odjela (43-oje učenika) sedmih razreda OŠ Lučac u Splitu. Istraživanje je pokazalo da postoji mala veličina učinka, tj. da primjena ovakvog oblika nastave ne pridonosi statistički značajno povećanju učinkovitosti u procesu učenja i poučavanja. Uporaba suvremenih tehnologija u nastavi hrvatskoga jezika u kombinaciji s tradicionalnom nastavom bio bi najbolji izbor jer pomaže učiteljima da lakše organiziraju kvalitetnu nastavu., This paper presents the results of a research on the impact of using the system Moodle in learning and teaching seventh graders the domain knowledge of the course in the Croatian language. The research was conducted by the Croatian language teachers and the second year graduate students from the Faculty of Natural Sciences and Mathematics, University of Split, that have enrolled on the course “Evaluation of e-learning systems” in the winter semester of the academic year 2011/2012. The sample included two seventh grade classes (43 students) from Elementary School of Lučac in Split. The research has shown that there is a small effect size, i.e. the usage of this form of teaching does not contribute significantly to improving the efficiency in the learning and teaching process. The use of modern technologies in teaching Croatian in combination with traditional approaches would be the best choice as this helps teachers to organize high-quality teaching more easily.

We present results of empirical evaluation of intelligent tutoring systems (ITS) with ontological domain knowledge representation. This research was done as a first step in the process of developing a new model of intelligent tutoring system that will include all the characteristics of evaluated systems: adaptive content, communication based on controlled natural language, graphical presentation of ontological domain knowledge representation. The case study results revealed extraordinary effectiveness of evaluated adaptive intelligent tutoring systems when compared with traditional learning and teaching process.

In this research we propose a comprehensive set of knowledge indicators aimed to enhance learners’ selfreflection and awareness in the learning and testing process. Since examined intelligent tutoring systems do not include additional messaging features, the introduction of common set of knowledge indicators differentiates our approach from the previous studies. In order to investigate the relation between proposed knowledge indicators and learner performance, the correlation and regression analysis were performed for 3 different courses and each examined intelligent tutoring system. The results of correlation and regression analysis, as well as learners’ feedback, guided us in discussion about the introduction of knowledge indicators in dashboard-like visualizations of integrated intelligent tutoring system.