Competence-based Knowledge Space Theory Research Articles

Empirical indistinguishability: From the knowledge structure to the skills.

Recent literature has pointed out that the basic local independence model (BLIM) when applied to some specific instances of knowledge structures presents identifiability issues. Furthermore, it has been shown that for such instances the model presents a stronger form of unidentifiability named empirical indistinguishability, which leads to the fact that the existence of certain knowledge states in such structures cannot be empirically tested. In this article the notion of indistinguishability is extended to skill maps and, more generally, to the competence-based knowledge space theory. Theoretical results are provided showing that skill maps can be empirically indistinguishable from one another. The most relevant consequence of this is that for some skills there is no empirical evidence to establish their existence. This result is strictly related to the type of probabilistic model investigated, which is essentially the BLIM. Alternative models may exist or can be developed in knowledge space theory for which this indistinguishability problem disappears.

Modeling learning in knowledge space theory through bivariate Markov processes

Bivariate Markov processes (BMPs) described by Ephraim and Mark (2012) consist of a pair of stochastic processes in the continuous time, one observable and the other latent, that are jointly Markov. In the present article, the navigation behavior and the learning process of a user of a web-based tutoring system are jointly modeled as BMPs constrained by assumptions that are coherent with the concepts of competence-based knowledge space theory. Such constraints are expressed as formal assumptions about the web-based system and about the nature of the learning process. Scenarios are considered where the observed process is the navigation of an individual through the pages of an intelligent tutoring system, whereas the latent learning process consists of transitions among states in a competence structure. The approach seems to be rather general and flexible in modeling learning scenarios with different assumptions. As an example, BMP models are developed for some exemplary scenarios. Maximum likelihood parameter estimation via expectation–maximization algorithm is presented. The results of a simulation study showed that the parameter values are well-recovered by the estimation algorithm. The results of the application of a bivariate Markov model to the real data of students navigating the intelligent tutoring system Stat-Knowlab showed that the proposed approach provides useful insight into students’ learning processes.

Stat-Knowlab. Assessment and Learning of Statistics with Competence-based Knowledge Space Theory

An intelligent tutoring system for learning basic statistics, called Stat-Knowlab, is presented and analyzed. The algorithms implemented in the system are based on the competence-based knowledge space theory, a mathematical theory developed for the formative assessment of knowledge and learning. The system’s architecture consists of the two assessment and learning modules that interact with each other in a continuous exchange of information about the current knowledge state of a student. This allows the system to personalize the student’s learning, providing only with the learning objects that she is ready to learn. During the browsing of the system, several types of navigation data are recorded. In this work, we analyzed data from two studies that were aimed at examining the learning processes induced by the navigation of the system. The results of both studies highlighted that the system is useful for monitoring the student learning processes during a university course of basic statistics.

On the assessment of learning in competence based knowledge space theory

The objective of an assessment in competence based-knowledge space theory (Cb-KST) is to infer the skills of an individual from her responses to a subset of problems. A major issue in this approach is the lack of a one-to-one correspondence between the competence states and performance states. The assessment is possible, but it cannotgo beyond an approximation. The problem becomes even more serious if Cb-KST is used for the assessment of learning, since changes caused at the competence level may not be represented by changes at the performance level. The consequence is that there could be no way for establishing whether learning has occurred or not. This impasse can be resolved for the class of conjunctive skill functions by pretending that the competence space induced by the skill function is well-graded. Under this condition an individual can make tangible progresses along the performance structure by learning one skill at a time, until full mastery is eventually reached. If the competence structure is a space satisfying certain compatibility conditions and the skill function satisfies a special property, called exclusiveness, then well-gradedness can be assured. A test of exclusiveness of a conjunctive skill function is described and exemplified.

Suitability of Adaptive Self-Regulated e-Learning to Vocational Training

Many studies have been conducted, mainly in a university environment, and researchers have identified both advantages and disadvantages of e-learning. Very little is known about the applicability and suitability of e-learning to vocational and skills-based training. The research presented in this paper evaluates an adaptive e-learning model (INNOVRET) which combines skills-based learning by means of the Competence based Knowledge Space Theory (CbKST) with the principles of self-regulated learning (SRL) for a practically-oriented vocational training area, namely highly skilled heat pump system installation, in Ireland. The research methodology employed to carry out this study consists of a participative study to develop the INNOVRET approach for e-learning for heat pump systems installation and an empirical study carried out to evaluate the INNOVRET system. The results show that it is the IT skills of the installers that determine the way they perceive the system and the whole learning experience, as well as the level of knowledge acquired.

Gamification and Smart Feedback

Gamification is a recent trend in the field of game-based learning that accounts for development effort, costs, and effectiveness concerns of games. Another trend in educational technology is learning analytics and formative feedback. In the context of a European project the developed a light weight tool for learning and practicing divisions named Sonic Divider. This simple app is based on features of gamification. More importantly, it features formative assessment and feedback functions based on Competence-based Knowledge Space Theory. The authors applied and evaluated the tool in Austrian classrooms and found some evidence for the motivational aspect of the gamification elements, in particular scoring. They also found positive effects of an individualized and meaningful feedback about errors. Finally, there occurred certain gender difference, for example, girls were much less attracted by competition elements (e.g., by comparing high scores) then boys, however, more attentive towards feedback coming from the tool.

Innovative Online Vocational Training of Renewable Energy Technologies (INNOVRET)

 Abstract— Heat pump systems are very important at a European level as they help meet the 20-20-20 targets of the EU policy. In recent years, the European market for these technologies has increased constantly. In addition to having insufficient numbers qualified in heat pump systems installation, current training has been recognised to have limitations. If high quality, standardised training in heat pump systems is not provided, it could result in a drawing back of the market development of heat pump systems in the EU. Furthermore, the low efficiency, poor reliability and increased cost of heat pump system installation and maintenance, which has resulted from poor training has also resulted in disillusionment on the part of the purchaser, notwithstanding the obstacles of time, cost and distance on the part of the installer with regard to training. INNOVRET (Innovative Online Vocational Training of Renewable Energy Technologies), a Leonardo da Vinci funded project, aims to address the need for training sufficient numbers in heat pump system installation throughout the EU. The innovative online training solution developed by the INNOVRET team will overcome the time, cost and distance constraints of learners through the delivery of the training in a flexible and accessible way. The methodological approach to training delivery is based on the Competence-based Knowledge Space Theory (CbKST). CbKST is a framework for representing the conceptual organisation of a given body of competences, which supports self-regulated learning. By developing competencies in the installation and maintenance of heat pump systems, installers and maintenance personnel all over Europe will be able to leverage these capabilities in order to increase their employability in the emergent heat pump system sector.

The simplified updating rule in the formalization of digital educational games

Competence-based Knowledge Space Theory (CbKST) has been proven to be a very well-fitting basis for realizing personalization in technology-enhanced learning. Especially in the area of game-based learning, however, some extensions and improvements are needed.Personalization in a serious game cannot be regarded simply as the selection of game assets according to the individual learner's current competences but it must also pay heed to the up-keeping of a storyline, it must be ensured that no part of the story is omitted that may be necessary to understand a later part. Therefore, a CbKST-compatible Markovian model for storytelling is proposed.A second issue is the ongoing, non-invasive assessment of the learner's current competences during the game. Every action of the learner within the game should be taken into account for the competence assessment, and the assessment must be done in real-time, i.e. there must not be any delay caused by the assessment which would interrupt the flow of the game. A simplified update procedure for competence assessment within CbKST is suggested which can solve this issue, and simulation results are presented comparing the new procedure with the classical one.

A methodology for eliciting, modelling, and evaluating expert knowledge for an adaptive work-integrated learning system

We present a methodology for constructing and evaluating models for adaptive informal technology-enhanced workplace learning. It is designed for knowledge-intensive work domains which are not pre structured according to a fixed curriculum. We extend research on Competence-based Knowledge Space Theory which has been mainly applied in educational settings. Our approach employs systematic knowledge elicitation and practically feasible evaluation techniques performed as part of the modelling process for iterative refinement of the models. A case study was performed in the Requirements Engineering domain to apply and test the developed methodology. We discuss lessons learned and several implications for knowledge engineering for adaptive workplace learning.

Supporting Self-Regulated Personalised Learning through Competence-Based Knowledge Space Theory

This article presents two current research trends in e-learning that at first sight appear to compete. Competence-Based Knowledge Space Theory (CBKST) provides a knowledge representation framework which, since its invention by Doignon & Falmagne, has been successfully applied in various e-learning systems (for example, Adaptive Learning with Knowledge Spaces [ALEKS] and Enhanced Learning Experience and Knowledge Transfer [ELEKTRA]), providing automated personalisation to learners' current knowledge and competence levels. Principles of self-regulated learning (SRL), pioneered by, for example, Zimmerman, however, argue for increased learner control, thus resulting in giving learners greater responsibility over their e-learning. The research presented in this article shows that skill-based visualisations in the tradition of CBKST and SRL-based autonomy are in no way conflicting but rather complement each other towards an integrated approach of self-regulated personalised learning. The research has been carried out and technologically translated into a set of visual tools for supporting the whole learning cycle within the scope of the iClass project.

Activity- and taxonomy-based knowledge representation framework

Elaborations of Competence-based Knowledge Space Theory (CbKST) incorporate skills that refer to the conceptual information of the domain as well as to the activities learners are expected to perform in this context. Thus, they are suggested as a formal knowledge representation framework that is able to take into account current activity-oriented pedagogical trends in designing effective Units of Learning (UoL). The broad array of required behaviour to be achieved by learners demands a search for instruments like taxonomies that allow for conceptualising activities, and consequently, skills and learning objectives. It is shown that the availability of such a taxonomy-based framework may be utilised in order to enhance the access and interface functionalities of learning systems. In particular, the selection of proper learning units and the delivery of effective feedback mechanisms on the teaching and learning progress are facilitated.