Context Aware e-Learning Objects and their Types from the Perspective of the Object-Oriented Paradigm

The contribution of this article demonstrates how to identify context-aware types of e-Learning objects (eLOs) derived from the subject domains. This perspective is taken from an engineering point of view and is applied during requirements elicitation and analysis relating to present work in constructing an object-oriented (OO), dynamic, and adaptive model to build and deliver packaged e-Learning courses. Consequently, three preliminary subject domains are presented and, as a result, three primitive types of eLOs are posited. These types educed from the subject domains are of structural, conceptual, and granular nature. Structural objects are responsible for the course itself, conceptual objects incorporate adaptive and logical interoperability, while granular objects congregate granular assets. Their differences, interrelationships, and responsibilities are discussed. A major design challenge relates to adaptive behaviour. Future research addresses refinement on the subject domains and adaptive hypermedia systems.

Decision Support Models for Composing and Navigating through e-Learning Objects

Libraries of learning objects may serve as basis for deriving course offerings that are customized to the needs of different learning communities or even individuals. Several ways of organizing this course composition process are discussed. Course composition needs a clear understanding of the dependencies between the learning objects. Therefore we discuss the metadata for object relationships proposed in different standardization projects and especially those suggested in the Dublin Core Metadata Initiative. Based on these metadata we construct adjacency matrices and graphs. We show how Gozinto-type computations can be used to determine direct and indirect prerequisites for certain learning objects. The metadata may also be used to define integer programming models which can be applied to support the instructor in formulating his specifications for selecting objects or which allow a computer agent to automatically select learning objects. Such decision models could also be helpful for a learner navigating through a library of learning objects. We also sketch a graph-based procedure for manual or automatic sequencing of the learning objects.

Forget about theory - practice is all? - Student teachers' learning in practicum

In 1999, all student teachers at secondary I level at the University of Bern who had to undertake an internship were asked to participate in a study on learning processes during practicum: 150 students and their mentors in three types of practicum participated—introductory practicum (after the first half‐year of studies), intermediate practicum (after two years of studies) and final practicum (after three years of studies). At the end of the practicum, student teachers and mentors completed questionnaires on preparing, teaching and post‐processing lessons. All student teachers, additionally, rated their professional skills and aspects of personality (attitudes towards pupils, self‐assuredness and well‐being) before and after the practicum. Forty‐six student teachers wrote daily semi‐structured diaries about essential learning situations during their practicum. Results indicate that in each practicum students improved significantly in preparing, conducting and post‐processing lessons. The mentors rated these changes as being greater than did the student teachers. From the perspective of the student teachers their general teaching skills also improved, and their attitudes toward pupils became more open. Furthermore, during practicum their self‐esteem and subjective well‐being increased. Diary data confirmed that there are no differences between different levels of practicum in terms of learning outcomes, but give some first insight into different ways of learning during internship.

Performance of undergraduate dental students on ICDAS clinical caries detection after different learning strategies

AIM This study aimed to evaluate the effect of a digital learning tool on undergraduate dental students' performance in detecting dental caries using ICDAS. METHODS An experimental digital learning tool (DLT) was created using digital photographs of sound and carious teeth. Thirty-nine students were divided into three groups (n = 13) and each assessed 12 randomly allocated patients before and after learning strategies: G1, ICDAS e-learning program; G2, ICDAS e-learning program plus DLT; G3, no learning strategy. Students (n = 32) reassessed patients 2 weeks after training. RESULTS Comparing before and after the learning strategies, any difference in the values of specificity and area under the ROC curve for all groups was found. Sensitivity was statistically significantly higher for G1 and G2. Comparing the groups, G2 showed a significant increase in sensitivity at the D2 and D3 thresholds. Spearman's correlations with the gold standard before and after the learning strategy were 0.60 and 0.61 for G1, 0.57 and 0.63 for G2, and 0.54 and 0.54 for G3, respectively. The Wilcoxon test showed a statistically significant difference between the values obtained before and after learning strategies for G1 and G2. CONCLUSIONS Use of the DLT after the ICDAS e-learning program tended to increase the sensitivity of ICDAS used by undergraduate dental students. The DLT appeared to improve dental students' ability to use ICDAS.

Object-Based Customization of Learning Content: Potentials and Pitfalls

Specification consortia and standardization bodies concentrate on e-Learning objects to en-sure reusability of content. Learning objects may be collected in a library and used for deriv-ing course offerings that are customized to the needs of different learning communities. How-ever, customization of courses is possible only if the logical dependencies between the learn-ing objects are known. Metadata for describing object relationships have been proposed in several e-Learning specifications. This paper discusses the customization potential of e-Learning objects but also the pitfalls that exist if content is customized inappropriately.

Index of Complexity, Outcome and Need scored on plaster and digital models

The aim of this study was to compare standard plaster models with their digital counterparts for the applicability of the Index of Complexity, Outcome, and Need (ICON). Generated study models of 30 randomly selected patients: 30 pre- (T(0)) and 30 post- (T(1)) treatment. Two examiners, calibrated in the ICON, scored the digital and plaster models. The overall ICON scores were evaluated for reliability and reproducibility using kappa statistics and reliability coefficients. The values for reliability of the total and weighted ICON scores were generally high for the T(0) sample (range 0.83-0.95) but less high for the T(1) sample (range 0.55-0.85). Differences in total ICON score between plaster and digital models resulted in mostly statistically insignificant values (P values ranging from 0.07 to 0.19), except for observer 1 in the T(1) sample. No statistically different values were found for the total ICON score on either plaster or digital models. ICON scores performed on computer-based models appear to be as accurate and reliable as ICON scores on plaster models.