Automatic Feedback Framework for Deriving Educational Ontologies

2012

Different approaches that use ontology as a tool for generating, organizing and personalizing e-learning content can be found in literature. In the context of e-assessment, the notion of ontology has been used for different purposes. One of them is the use of ontologies as a mean of providing a structure to guide the automated design of assessments. Despite of the effort done in this direction, there is still a lot of work to be done since the most of the approaches have proposed lightweight ontologies that model a part of the assessment domain without considering pedagogical aspects. This paper presents a heavyweight ontology, called EAOnto, that conceptualizes the eassessment domain including a set of rules representing pedagogical principles that are worth to follow. In addition, an empirical evaluation is discussed.

2011

Semantic web technologies have been increasingly used as a tool for generating, organizing and personalizing e-learning content, including e-assessment. In this paper we discuss and extend an innovative approach to automated generation of computer-assisted assessment (CAA) from semantic web-based domain ontologies. We expand the work previously done in this area in two important directions: first, we add new ontology elements (annotations), to the meta-ontology used for generating questions; second, we add a semantic interpretation of the mapping between the ‘domain' ontology and the target ‘question' ontology. The semantic interpretation is based on the notion of ‘question templates' that are founded on Bloom's taxonomy of educational objectives, but can be sourced equally well in any other pedagogical premise applicable to question design and content (e.g. Kolb's learning theory). We use the examples obtained from the prototype implementation to show how that w...

Ijimai, 2012

During the last year, approaches that use ontologies, the backbone of the Semantic Web technologies, for different purposes in the assessment domain of e-Learning have emerged. One of these purposes is the use of ontologies as a mean of providing a structure to guide the automated design of assessments. The most of the approaches that deal with this problem have proposed individual ontologies that model only a part of the assessment domain. The main contribution of this paper is an ontology network, called AONet, that conceptualizes the e-assessment domain with the aim of supporting the semi-automatic generation of it. The main advantage of this network is that it is enriched with rules for considering not only technical aspects of an assessment but also pedagogic.

Journal of E-Learning and Knowledge Society

ABSTRACT

2005

WWW service has enabled development of e-learning systems that are considered to be direct application of the information and communication technology. xTEx-Sys system is an Web-based authoring shell with adapted environment to each actor of the system. Formal representation of course material in xTEx-Sys involves ontology driven knowledge description. Student's knowledge evaluation in xTEx-Sys is realized using dynamic quizzes. This paper describes ontology's role of xTEx-Sys knowledge evaluation process.

This paper presents an approach for the automated evaluation of the learner's state of knowledge in an intelligent tutoring system, based on a granular and ontological model of the teaching domain. This approach, called ODALA (Ontology-Driven Auto-Evaluation for Learning Approach), is now being implemented in different self-learning settings, particularly those concerned with algorithmic teaching. ODALA represents the teaching domain as a domain ontology, which not only allows the evaluation to be propagated in each of the domain's components, but also facilitates error classification and detection. The evaluation process includes an error diagnosis module, a marking process, and an update of the learner model, all of which produce a well-calculated knowledge of the state of learners at different abstraction levels. This paper also presents results of the development and experimentation of our algorithmic self-learning system.

Educational Sciences: Theory and Practice

Ontologies have often been recommended for E-learning systems, but few efforts have successfully incorporated student data to represent knowledge conceptualizations. Defining key concepts and their relations between each other establishes the backbone of our E-learning system. The system guides an individual student through his/her course by evaluating their progress and suggesting instructional material to review based upon their answers. Three main tasks are performed within this framework: building ontologies for the course, measuring a student's understanding level for the concepts, and making personal suggestions to create an individualized learning environment. This paper presents: the integration of ontologies, assisted with student data, together with an intelligent Recommendation Module for the development of an E-learning system; the comparison and correction adaption of ontology from students' mind maps; and the assessment of students' actual weaknesses in comparison to what Recommendation Module suggests. The sample of 127 students, five classrooms, was conveniently selected among seventh grade students of a demographically average school in a major city in Turkey. The students' achievement was assessed and the scores for different questions were investigated for associations with concepts made in the students' minds. The results provided significant correlations among scores, and a fit model for the concepts represented by questions. The student suggested model slightly differed from the ontology map from the experts. Based on the data-supported model, the Recommendation Module more accurately determined the students' learning deficiencies and suggested concepts to be reviewed.

2010

Tutoring systems typically contain or generate a set of approved solutions to problems presented to students. Student solutions that don't match the approved ones, but are otherwise partially correct, receive little acknowledgment as feedback, stifling broader reasoning. Additionally, feedback mechanisms rely on having the student model, which requires extensive effort to build. This paper provides an alternative to the traditional ITS architecture by using a hint generation strategy that bypasses the student model and instead leverages off of the domain ontology. Concept hierarchy and co-occurrence between concepts in the domain ontology are drawn upon to ascertain partial correctness of a solution and guide student reasoning towards the correct solution. We describe the strategy incorporated in a tutoring system for medical PBL, wherein the widely available UMLS is deployed as the domain ontology. Evaluation of expert agreement with system generated hints on a 5-point likert scale resulted in an average score of 4.44 (r = 0.9018, p < 0.05). Hints containing partial correctness feedback scored significantly higher than those without it (Wilcoxon Rank Sum, p < 0.001).

2004

Upgrading the level of knowledge is one of the most important objectives in education and pedagogy. In this paper we propose a framework for an ontology-based interactive tutoring system. System is composed of four interconnected components, a tutor module, a light-weight student modeler, an assessor and a reasoning engine. The system is fully automated as tries to tutor a domain ontology without a specific curriculum or topic ontology. Our proposed approach gives extra authority to the users to explore all available information and switch between learning and evaluation based on their preferences. Thus, it seams to be more appropriate for intermediate students that have preliminary knowledge about the domain and not for novitiates.

J. Educ. Technol. Soc., 2015

Introduction It is well known that e-learning has become a popular and growing business, thus, the number of courses available on Internet is growing rapidly. Among factors affecting e-learning success, proper feedback mechanisms are integral. As Dewey (1963) argues, learning is fundamentally social in nature. Thurmond, Wambach, Connors and Frey (2002) state that diversity in assessment and perception of interaction with others influences e-learning satisfaction considerably. The use of different evaluation methods in an e-learning system causes learners to establish a connection between themselves and their instructors, which in turn helps to ensure that their learning efforts are properly assessed (Sun Tsai, Finger, Chen, & Yeh, 2008). There are a variety of instruments for assessment; however, in an e-learning system, only a few of them are generally used, with the most popular being objective instruments such as multiple and simple choice. In the area of e-assessment, there are ...