Supporting Children in Mastering Temporal Relations of Stories

Though temporal reasoning is a key factor for text comprehension, existing proposals for visualizing temporal information and temporal connectives proves to be inadequate for children, not only for their levels of abstraction and detail, but also because they rely on pre-existing mental models of time and temporal connectives, while in the case of children the system has to induce the development of a mental model not existing yet. Filling this gap was the main goal of the FP7 European project TERENCE, which developed an adaptive learning system shaped around the concepts of repeated interaction experience and of graded text simplification and consistent with consolidated pedagogical approaches built on question-based games. In particular, in this paper the authors present the main features of its learner-oriented read-and-play visual interaction environment that, according to the dual-coding theory, follows a two-tiers approach pairing verbal and visual information.

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The Design of Learning Material for Poor Comprehenders: Lessons Learnt from Experts

Jurnal Teknologi ◽

10.11113/jt.v63.1953 ◽

2013 ◽

Vol 63 (3) ◽

Author(s):

Vincenza Cofinia ◽

Fernando De la Prieta ◽

Tania Di Mascio ◽

Rosella Gennari ◽

Ivana Marenzi ◽

...

Keyword(s):

Artificial Intelligence ◽

Adaptive Learning ◽

Text Comprehension ◽

Learning System ◽

Learning Material ◽

Or Education ◽

Domain Experts ◽

Lessons Learnt ◽

Poor Comprehenders ◽

Adaptive Learning System

TERENCE is an FP7 ICT European project that is developing an adaptive learning system for poor comprehenders and their educators. The learning material is made of stories and smart games for stimulating reading comprehension. The design of stories and smart games is also based on data collected from experts for the analysis of the context of use of the system, and is incrementally revised via evaluations of prototypes of stories and games, with domain experts of text comprehension or education as participants. In particular, since smart games are semi-automatically generated via artificial intelligence technologies, they contain mistakes that have to be fixed by experts of pedagogy before the games are given to learners. In this paper we focus on the design and evaluations of the TERENCE stories and smart games for poor comprehenders via lessons learnt with domain experts.

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Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.2.4.158 ◽

2018 ◽

Vol 2 (4) ◽

pp. 271 ◽

Cited By ~ 3

Author(s):

Outmane Bourkoukou ◽

Essaid El Bachari

Keyword(s):

Learning Style ◽

Recommender System ◽

Adaptive Learning ◽

Learning Objects ◽

Research Field ◽

Learning System ◽

K Nearest Neighbors ◽

E Learning ◽

Hybrid Recommender System ◽

Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

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