Mieke Vandewaetere

Review: The contribution of learner characteristics in the development of computer-based adaptive learning environments

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.

Introducing psychometrical validation of questionnaires in CALL research: The case of measuring attitude towards CALL

The great majority of questionnaires measuring non-observable constructs such as attitude towards CALL are often developed from a specific point of view and are seldom followed by psychometrical validation. Psychometrical properties of the questionnaire, such as construct validity and reliability, then remain unanswered too often, laying a heavy burden upon the generalizability of the obtained results. In light of recent calls for more theory construction in CALL it would be very fruitful if researchers start to work with validated operationalizations of the variables they work with in developing CALL theories. The research presented in this paper aims at introducing a methodological approach to develop an empirically-based and psychometrically-sound instrument to measure the attitude towards computer-assisted learning (A-CAL), attitude towards foreign language learning (A-FLL) and, more specifically, attitude towards computer-assisted language learning (A-CALL).

4C/ID in medical education: How to design an educational program based on whole-task learning: AMEE Guide No. 93

Abstract Medical education increasingly stresses that medical students should be prepared to take up multiple roles as a health professional. This requires the integrated acquisition of multiple competences such as clinical reasoning and decision making, communication skills and management skills. To promote such complex learning, instructional design has focused on the use of authentic, real-life learning tasks that students perform in a real or simulated task environment. The four-component instructional design model (4C/ID) model is an instructional design model that starts from the use of such tasks and provides students with a variety of learning tools facilitating the integrated acquisition of knowledge, skills and attitudes. In what follows, we guide the reader on how to implement educational programs based on the 4C/ID model and illustrate this with an example from general practice education. The developed learning environment is in line with the whole-task approach, where a learning domain is considered as a coherent, integrated whole and where teaching progresses from offering relatively simple, but meaningful, authentic whole tasks to more complex tasks. We describe the steps that were taken, from prototype over development to implementation, to build five learning modules (patient with diabetes; the young child with fever; axial skeleton; care for the elderly and physically undefined symptoms) that all focus on the integrated acquisition of the Canadian Medical Education Directives for Specialists roles in general practice. Furthermore, a change cycle for educational innovation is described that encompasses practice-based challenges and pitfalls about the collaboration between different stakeholders (students, developers and teachers) and the transition from traditional, fragmented and classroom-based learning to integrated and blended learning based on sound instructional design principles.

What can the same-different task tell us about the development of magnitude representations?

We examined the development of magnitude representations in children (Exp 1: kindergartners, first-, second- and sixth graders, Exp 2: kindergartners, first-, second- and third graders) using a numerical same-different task with symbolic (i.e. digits) and non-symbolic (i.e. arrays of dots) stimuli. We investigated whether judgments in a same-different task with digits are based upon the numerical value or upon the physical similarity of the digits. In addition, we investigated whether the numerical distance effect decreases with increasing age. Finally, we examined whether the performance in this task is related to general mathematics achievement. Our results reveal that a same-different task with digits is not an appropriate task to study magnitude representations, because already late kindergarteners base their responses on the physical similarity instead of the numerical value of the digits. When decisions cannot be made on the basis of physical similarity, a similar numerical distance effect is present over all age groups. This suggests that the magnitude representation is stable from late kindergarten onwards. The size of the numerical distance effect was not related to mathematical achievement. However, children with a poorer mathematics achievement score seemed to have more difficulties to link a symbol with its corresponding magnitude.

Cognitive Load of Learner Control: Extraneous or Germane Load?

Computer-based learning environments become more tailored when learners can exert control over one or more parts of the learning process. Learner control (LC) demands additional efforts of learners because, in addition to learning, they also have to monitor that learning. As a consequence, LC may cause additional cognitive load and even cognitive overload. The central question in this study is what type of cognitive load is induced by LC and whether the experienced load is related to learning outcomes. For this study, half of the students had control over task selection, while the other half had not. Within each condition, students were assigned to a single treatment, with the primary task to solely focus on the learning content, and a dual treatment, comprising a primary task and a secondary task. The results indicate that LC did not impose higher cognitive load as measured by secondary task scores and mental effort ratings.

Ontology-Driven Adaptive and Pervasive Learning Environments – APLEs: An Interdisciplinary Approach

This paper reports an interdisciplinary research project on adaptive and pervasive learning environments. Its interdisciplinary nature is built on a firm collaboration between three main research domains, namely, instructional science, methodology, and computer science. In this paper, we first present and discuss mutual, as well as distinctive, vision and goals of each domain from a computer science perspective. Thereafter, we argue for an ontology-driven approach employing ontologies at run-time and development-time where formalized ontologies and rules are considered as main medium of adaptivity, user involvement, and automatic application development. Finally, we introduce a prototype domain context ontology for item-based learning environments and demonstrate its run-time and development-time uses.

Integration in the curriculum as a factor in math-game effectiveness

While numerous claims are made about the effectiveness of games, the studies that examine their educational effectiveness often contain flaws resulting in unclear conclusions. One possible solution for these shortcomings is to focus on separate game elements rather than on games as a whole. A second solution is to take into account students’ perception as this is likely to affect students’ interpretations and learning outcomes. This study investigated the effect of the integration of an educational game in the curriculum on students’ motivation, perception, and learning outcomes. Forty-nine vocational track students participated, all working in a game-based learning environment for learning calculations with fractions. The results demonstrate that integrating the learning content in the game with the learning content in the classroom is related to students’ in-game performance, but not to students’ math performance on a paper-and-pencil test, postgame perception and postgame motivation. To conclude this chapter, practical and theoretical implications for the fields of instructional design and educational games research are discussed.

Performance in Educational Math Games: Is It a Question of Math Knowledge?

In order to develop game-based learning environments (GBLEs) that accommodate to learners’ needs and individual differences, GBLEs can be enriched with learner models that describe learner profiles from which adaptive instruction can be offered during gameplay. Learner models can encompass several parameters or learner characteristics derived from measurements taken either prior to play (e.g., already available knowledge of the subject matter of which the GBLE is comprised) or during gameplay (i.e., learner behavior in the GBLE). This study makes a case for two skills which may be relevant from the perspective of adaptive gameplay, namely (1) the knowledge or skills with respect to the learning content and (2) the gaming skills. The current study investigates the joint inclusion of both gaming skills and domain knowledge creating learner profiles. In addition, this study sheds light on how performance during gameplay can be attributed to certain learner profiles. To investigate this, a commercially available 3D educational game for primary school children was offered to 53 children of the third grade. Learners’ behavior while playing in the GBLE was captured and logged. Prior to gameplay, math knowledge, and gaming skills were measured. Subsequently, learners’ in-game performance was measured. Results revealed that learners with high or low gaming skills can be distinguished into two learner profiles. More specific, learners with high gaming skills outperformed learners with low gaming skills in more complex mini-games. The findings of this study suggest that a learner’s gaming skills can be taken into account in developing learner profiles and hence in the design and development of GBLEs.

Perceptions and Illusions about Adaptivity and Their Effects on Learning Outcomes

While the research on adaptive learning environments is still flourishing, the relative effect of perceptions of adaptivity has to our knowledge not been sketched yet. Hence, it can be discussed whether program-defined adaptivity is only effective if perceived and experienced as such. In this study, we hypothesize that perceptions of adaptivity moderate the effectiveness of adaptive learning environments and that even the mere illusion of adaptivity can provide a means for improving learning outcomes. We report a pilot study in which the illusion of adaptivity was created while learners were using an online French language learning environment. The results of this study demonstrate that the illusion of adaptivity can be created in electronic learning environments and, moreover, that this illusion affects learning outcomes. We discuss the role of perceptions in learning and possibilities and limits of perceptions in adaptive learning environments.

Learner Control on Feedback: A New Extension to Adaptive Learning?

We present the design of a study in the field of feedback in adaptive learning environments. The study emphasizes the role of the learners and the control they can exert over the level of feedback. However, not all learners equally benefit from learner control and, hence, individual differences need to be taken into account. We discuss the research questions, the design and methodology of this study. We suggest that both learner characteristics (such as motivation and prior knowledge) and item characteristics (item difficulty level) have differential effects on learner control over feedback.