Dietrich Albert

Micro-adaptivity: protecting immersion in didactically adaptive digital educational games

The idea of utilizing the rich potential of todays computer games for educational purposes excites educators, scientists and technicians. Despite the significant hype over digital game-based learning, the genre is currently at an early stage. One of the most significant challenges for research and development in this area is establishing intelligent mechanisms to support and guide the learner, and to realize a subtle balance between learning and gaming, and between challenge and ability on an individual basis. In contrast to traditional approaches of adaptive and intelligent tutoring, the key advantage of games is their immersive and motivational potential. Because of this, the psycho-pedagogical and didactic measures must not compromise gaming experience, immersion and flow. In the present paper, we introduce the concept of micro-adaptivity, an approach that enables an educational game to intelligently monitor and interpret the learners behaviour in the games virtual world in a non-invasive manner. On this basis, micro-adaptivity enables interventions, support, guidance or feedback in a meaningful, personalized way that is embedded in the games flow. The presented approach was developed in the context of the European Enhanced Learning Experience and Knowledge TRAnsfer project. This project also realized a prototype game, demonstrating the capabilities, strengths and weaknesses of micro-adaptivity.

STOCHASTIC CHOICE HEURISTICS

Abstract A class of stochastic model is presented which assume that binary choice among multidimensional alternatives is accomplished by sequential accumulation of dimensional value differences until enough evidence is accumulated. The models purpose not only to predict individual choices or choice probabilities from dimensional evaluations of the alternatives but also to map the cognitive processes taking place. The latter conjecture is tested by an analysis of choice latencies. Within the model class competing assumptions are made about the source of fluctuations in choice behavior (fluctuations in values versus fluctuations in the selection of dimensions) and about the dimensional comparison process (cardinal versus ordinal). The models are tested with individual choice probabilities and latencies from eight subjects making many choices between alternatives in four topic fields. The models do better when alternatives are described on a number of dimensions than for alternatives presented only by name. Further, the results suggest that fluctuations in the selection of dimensions must be seriously considered as an alternative to fluctuations in values for explaining choice variability.

Extending eductional metadata schemas to describe adaptive learning resources

This paper describes a generic technique for representing Adaptive Learning Resources by extending current metadata schemas. The requirement for the work described here has grown out of the necessity to facilitate accurate discovery and integration of Adaptive Learning Resources, namely Adaptive Hypermedia Services.

Establishing knowledge spaces by systematical problem construction

Procedures which are to test a subject’s knowledge concerning a specific domain obviously require (in addition to other prerequisites) a set of problems.

Cognitive Choice Processes and the Attitude-Behavior Relation

Fishbein and Ajzen’s (1975) theory of reasoned action is currently the most successful and much discussed theory in attitude-behavior research. Although the theory is well established for predicting behavior in various contexts, its central assumption of a static attitude-behavior intention relation appears questionable when recent results of cognitively oriented decision research are considered. This assumption is dynamically reformulated in order to model the cognitive processes in binary choice. The resulting criterion-dependent choice models assume a sequential evaluative processing of the alternatives’ attributes until enough evidence in favor of one alternative is accumulated. Within this general framework, specific models distinguish between internally (memory) and externally (display) available information about the alternatives. The models and a series of experimental studies, which were specifically designed to investigate various aspects of the models, are comprehensively presented. Relations to other theories and consequences for attitude-behavior research are discussed.

The ELEKTRA ontology model: a learner-centered approach to resource description

There is little doubt that intelligent and adaptive educational technologies are capable of providing personalized learning experiences and improving learning success. Current challenges for research and development in this field concern, for example, the design of comprehensive data models for adaptive systems as well as the interoperability of systems and the reusability of learning material across different systems. In the present work we introduce an ontology model, basically developed in the context of immersive digital games, which attempts to provide a solution to existing problems in resource description. On the one hand, comprehensive data models for adaptive systems are supported by separating static information from adaptive systems as far as possible. On the other hand, the ontology model offers a potential solution to precise and, above all, learner-centered resource description by separating latent competencies from observable performance (in learning objects or test items).

Construction of Knowledge Spaces for Problem Solving in Chess

An extension of the theory of knowledge spaces introduced by Doignon and Falmagne (1985) is presented. This extension allows surmise relations and quasi-ordinal knowledge spaces to be constructed with the help of the formal principles ‘set inclusion’2 and ‘sequence inclusion’ from basic units of knowledge. The basic units for constructing the surmise relation in chess are the tactical elements of the game — the ‘motives’. In terms of problem solving, these motives can be regarded as subgoals in the process of problem solving. Two experimental investigations of chess knowledge test the empirical validity of the two principles and show that the theory of knowledge spaces is suitable for testing psychological models.

Individualized Skill Assessment in Digital Learning Games: Basic Definitions and Mathematical Formalism

The assessment of knowledge and learning progress in the context of game-based learning requires novel, noninvasive, and embedded approaches. In the present paper, we introduce a mathematical framework which relates the (problem solution) behavior of a learner in the game context to the learners available and lacking competencies. We argue that a problem situation and its status at a certain point in time can be described by a set of game props and their current properties or states. In the course of the game, the learner can perform different actions to modify the props and, consequently, change the problem situation. Each action is evaluated with respect to its correctness or appropriateness for accomplishing a given task which, in turn, enables conclusions about the competence state of the learner. This assessment procedure serves as the basis for adaptive interventions, for instance, by providing the learner with guidance or feedback.

Supporting competency development in informal workplace learning

This paper seeks to suggest ways to support informal, self-directed, work-integrated learning within organizations. We focus on a special type of learning in organizations, namely on competency development, that is a purposeful development of employee capabilities to perform well in a large array of situations. As competency development is inherently a self-directed development activity, we seek to support these activities primarily in an informal learning context. AD-HOC environments which allow employees context specific access to documents in a knowledge repository have been suggested to support learning in the workplace. In this paper, we suggest to use the competence performance framework as a means to enhance the capabilities of AD HOC environments to support competency development. The framework formalizes the tasks employees are working in and the competencies needed to perform the tasks. Relating tasks and competencies results in a competence performance structure, which structures both tasks and competencies in terms of learning prerequisites. We conclude with two scenarios that make use of methods established in informal learning research. The scenarios show how competence performance structures enhance feedback mechanisms in a coaching process between supervisor and employee and provide assistance for self directed learning from a knowledge repository.

Process Traces of Binary Choices: Evidence for Selective and Adaptive Decision Heuristics

While previous research has shown that human decision processes adjust to the characteristics of choice situations and task demands, the present study examined whether information processing would even adapt to the characteristics of an individual choice pair. By a process tracing method it was found that the amount of inspected information systematically depended upon the particular choice pair. In order to specify the selective and adaptive information processing, criterion-dependent choice models were introduced. These models postulate that information processing continues until the accumulated amount by which one alternative is better than the other reaches or exceeds a certain criterion. These models are strongly supported by the empirical data of the present study. Deviations between model predictions and observed data are explained in terms of fluctuating feature values of the choice alternatives.