Learner Model

The development of learner models takes an active part in upcoming adaptive learning environments. The purpose of learner models is to drive personalization based on learner and learning characteristics that are considered as important for the learning process, such as cognitive, affective and behavioral variables. Despite the huge amount of theoretical propositions of learner characteristics considered as relevant for learner models, practical payoffs are rather sparse. This study aims to overview the empirical research on the mere value of learner models in the development of adaptive learning environments. The results show that a lot of high-quality studies are situated in a rather shattered research field, building few bridges from theory to practice. We conclude with the call for a theory or framework integrating current and past research results that is able to guide theory-based and systematic empirical research having concrete hypotheses on the merits of learner characteristics in adaptive learning environments.

In this chapter we present P-Dinamet, a web-based educational system that recommends to the learner certain actions within a Physics course. We review an earlier version of the system and the pedagogical model behind it, then present an extended system that adapts to both learners and teachers. The sugges tions are made according the information stored in learners models containing information about personal data, preferences and knowledge level. These models are presented to the teacher along with information about the learners’ performance in the course and the suggestions to be presented to the learners. The teacher can modify the learners’ models and provide feedback to the adaptation mechanism.

Liquid retaining structures, which are mainly used to retain domestic water, sewage, or industrial waste, are usually constructed with reinforced concrete in Hong Kong. During the design, novice engineers often encounter problems including decision-making based on rules of thumb, judgment, previous experience, heuristics, and code of practice. A computer-aided tool that incorporates engineering judgment along with algorithmic tools is highly desirable. This paper presents an intelligent tutoring system, with hybrid knowledge representation techniques including production rule system and object-oriented approach, which provides expert advice on design of liquid retaining structures. Intelligent tutoring system is a type of knowledge-based expert system with the particular purpose of teaching. It can serve the purpose for one-to-one tutoring, which is generally acknowledged to be the most effective teaching method, yet requiring high ratio of resources. This system is based on British Standards Code of Practice BS8007: 1987: Design of concrete structures for retaining aqueous liquids. Training exercises, learner model as well as tailored explanations on real design practice are furnished to help inexperienced designers or civil engineering students to learn how to design liquid retaining structures. Nowadays, with the popularity of personal computer, civil engineering students as well as novice engineers can glean hand-on knowledge on design of liquid retaining structures more effectively through this system. An evaluation of the developed system and investigation of the effectiveness to learn the domain design knowledge are undertaken.

The DARWARS Tactical Language Training System (TLTS) helps learners acquire basic communicative skills in foreign languages and cultures. Learners practice their communication skills in a simulated village, where they must develop rapport with the local people, who in term will help them accomplish missions such as post-war reconstruction. Each learner is accompanied by a virtual aide who can provide assistance

Abstract. Research on inspectable learner models involves the study of representational aspects of the profile that systems maintain about learners. When learners and teachers interact with inspectable learner models several questions arise, such as: What kind of representation ...

Background: The development of learner models in learning management systems is among the most significant steps in designing personalized e-learning environments. The primary purpose of this modeling is to extract user characteristics in order to personalize the learning process based on learners’ needs, learning style, personality, and situation. Methods: The present study provides a review of published literature over the past 20 years in academic databases including IEEE, Sciencedirect, Wiley, and Springer. The search was limited to the studies on the personalization of e-learning environments based on learner characteristics, specifically the ones providing a reliable method for integrating these characteristics, as appropriate input variables, in the design of personalized e-learning systems. Results: This study proposed a new method of classifying the learner characteristics as the variables for designing a personalized e-learning system. A total of 111 papers were considered...

MOTS CLÉS : agent cognitif, émotion, conscience, modèle de l'apprenant, cognition, décision, adaptation. ABSTRACT : Recent studies reveal that emotions have much to do with learner's performances. Consequently, e-learning systems should integrate emotional capabilities in order to optimize learners' experience. This paper presents CTS, our cognitive agent as it is applied to tutoring. Based on Baars' Global Workspace theory on mind and consciousness, CTS mimics the processing accomplished by the brain. Its highly distributed competence lets the agent consider concurrently multiple information sources and analyse a situation from various points of view. Central to this paper is a proposed extension to CTS' emotional processing. It should allow CTS to take into consideration manifestations of learners' emotions along with his evolving knowledge. This aims both at adapting its pedagogical or didactical interventions, and at playing on learner's emotio...

Open learner models to facilitate reflection are becoming more common in adaptive learning environments. There are a variety of approaches to presenting the learner model to the student, and for the student to interact with their open learner model, as the requirements for an open learner model will vary depending on the aims of the system. In this paper we

Abstract. In this paper, we present the architecture of an adaptive web-based concept map assessment tool, named COMPASS, which aims to support the assessment as well as the learning process. The presentation of the main modules of the tool focuses on the representation of the domain knowledge and the learner model, the diagnosis of the learner’s knowledge and the generation of the adaptive feedback. Based on an assessment goal that the learner selects, COMPASS provides various assessment activities, which employ concept mapping tasks. The diagnosis process identifies and analyses the learner’s beliefs represented on his/her map in order to perform (i) a qualitative estimation of his/her knowledge based on a categorization of the different types of errors, and (ii) a quantitative estimation of his/her knowledge level based on specific assessment criteria. Aiming to stimulate learners to reflect on their beliefs, COMPASS incorporates different informative and tutoring feedback compon...

Abstract. Intelligent information handling support is crucial for the efficiency of web-based learning. This paper presents an approach to information handling, which implies conceptual support for both learners and instructors engaged in openlearning and teaching tasks in a web-based course support environment. The proposed approach combines two powerful techniques to support web-based course learning-information retrieval and concept mapping. It involves conceptualisation of the subject domain and representation ...

Background: The development of learner models in learning management systems is among the most significant steps in designing personalized e-learning environments. The primary purpose of this modeling is to extract user characteristics in order to personalize the learning process based on learners’ needs, learning style, personality, and individual circumstances. Methods: The present study provides a review of published literature over the past 20 years in academic databases including IEEE, Sciencedirect, Wiley, and Springer. The search was limited to the studies on the personalization of e-learning environments based on learner characteristics, specifically the ones providing a reliable method for integrating these characteristics, as appropriate input variables, in the design of personalized e-learning systems. Results: This study proposed a new method of classifying the learner characteristics as the variables for designing a personalized e-learning system. A total of 111 papers were considered for analysis. In the end, 22 influential learner characteristics were extracted and classified into six subcategories, namely cognitive, motivational, behavioral, emotional, metacognitive aspects, and combined domains. The proposed classification method was also compared with available related categorizations to demonstrate this method’s advantage in designing a personalized e-learning environment. Conclusion: The findings represent the learning criteria that can be utilized in designing adaptive learning systems. Moreover, it can also aid other researchers in this field to achieve a better perspective in learner modeling. Applying these characteristics as input design variables in personalized e-learning systems can result in a better solution for personalization.

This pilot study reports how to portray open learner models as animal companions in order to motivate children to learn in the digital classroom environment. To meet two challenges of motivation and interactivity for open learner models, the concept of open learner models as animal companions is proposed based on the emotional attachment of humans towards pets. Animal companions adopt

In this paper, we discuss how externalising learners' interaction be- haviour may support learners' explorations in an adaptive educational hyper- media environment that provides activity-oriented content. In particular, we collect raw data from learners' interaction, model the state of interaction using a set of indicators and contextual information, and visualize this information alongside with comparative information coming from the instructor or col- leagues. This way we provide learners with a mirror of their behaviour and relative measures such as instructor's proposals or peers' behaviour, aiming (a) to promote learners' reflection on their learning and support them self- diagnose the efficacy of their interaction; (b) to help learners to plan their learning; (c) to facilitate collaboration because learners can improve under- standing of themselves and each other, and select appropriate partners; (d) to support tutors in providing personalised gu...

This paper describes a multi -agent, personalised learning system operating over the Web. The system is called Web F-SMILE and is meant to help novice users learn how to manipulate the file store of their personal computer. In order to provide adaptive help and tutoring, Web F-SMILE has assigned an agent to constantly observe the user and collect information about him/her. This information is maintained centrally on a Learner Modelling Server. In this way, each learner model is available to any client application that requests it. The agents of the client applications interact with the Learner Modelling Server through Web Services. The main characteristic of Web Services is that they interact with the applications that invoke them, using web standards. Basing learner modelling on web standards has the advantage of enabling the dynamic integration of applications distributed over the Internet, independently of their underlying platforms.