The paper describe the development of an e-learning package to assist in teaching literature searching, referencing and avoiding plagiarism (information skills) to investigate the effectiveness of different feedback styles (corrective only, corrective explanatory, and video feedback) on student learning. Three version of the e-learning package were developed of which employed a different style of feedback in response to students’ answers to formative quizzes. Then experimental testing of the three packages by students who were using the package to support learning in all their modules. Both quantitative and qualitative data was collected to assess the effectiveness of the package. The results of the pre and post test-test quizzes showed a significant increase of 10%. However, this can also be attributed to other teaching activities students were engaged in at that time. The preliminary analysis of the results did not reveal a clear effect for the feedback styles, although the results suggest some variation in students ’ preference for feedback styles.

The coherence principle states that all non-essential information in multimedia messages should be eliminated to minimise demands on cognitive resources. This assertion has been empirically verified in controlled laboratory studies with learners who have little prior knowledge and limited interest in the domain of instruction. It has not been investigated, however, whether the coherence principle generalises to real learning environments. In this study, 104 students from year 10, year 11, and first year university viewed either a concise or an extended online multimedia treatment on stellar spectra. The extended treatment included additional interesting information about the formation of black holes, galaxy collisions and the observation of dark matter. Following the multimedia, participants completed a retention and transfer test that covered only the material common to both treatments. Results showed students in both treatment groups achieved similar performance. This suggests that in authentic learning settings, interest may mitigate the effects of the coherence principle. Difficulties involved in measuring differences in learning within the constraints of a real learning environment are also addressed.

Abstract: A set of computer-based experiments are reported that investigate the understanding achieved by learners when studying a complex domain (statistics) in a real E-learning environment using three different media combinations – Text only, Text and Diagrams and Spoken Text and Diagrams, and the results agree with earlier work carried out on more limited domains. The work is then extended to examine how student interaction and student learning styles affect the learning outcomes. Different responses to the media combinations are observed and significant differences occur between learners classified as Sensing and Reflective learners. The experiment also identified some important differences in performance with the different media combinations by students registered as Dyslexic. The experiment was therefore repeated with a much larger sample of Dyslexic learners and the earlier effects were found to be significant. The results were surprising and may provide useful guidance for the design of material for Dyslexic students. 1.

Abstract. In this paper we discuss and evaluate a method for automatic text illustration, applied to answers to medical questions. Our method for selecting illustrations is based on the idea that similarities between the answers and picture-related text (the picture’s caption or the section/paragraph that includes the picture) can be used as evidence that the picture would be appropriate to illustrate the answer. In a user study, participants rated answer presentations consisting of a textual component and a picture. The textual component was a manually written reference answer; the picture was automatically retrieved by measuring the similarity between the text and either the picture’s caption or its section. The caption-based selection method resulted in more attractive presentations than the section-based method; the caption-based method was also more consistent in selecting informative pictures and showed a greater correlation between user-rated informativeness and the confidence of relevance of the system. When compared to manually selected pictures, we found that automatically selected pictures were rated similarly to decorative pictures, but worse than informative pictures. 1

This paper describes two experiments carried out to investigate the production and evaluation of multimodal answer presentations in the context of a medical question answering system. In a production experiment participants had to produce answers to different types of questions. The results show that about one in four produced answers using multiple media. In an evaluation experiment, users had to evaluate different types of multimodal answer presentations. Answers with an informative visual were evaluated as more informative and more attractive than answers with a mere illustrative visual.

This work reviews several aspects of the growing research field interested in video games. First, the evolution of this media in the educational field is discussed. Three different fields interested in the cognitive impact playing of video games are reviewed: abilities and skills, attitudes and motivation, knowledge and content learning. However, most studies used video games as new experimental materials and tasks to contribute to their specific field (i.e. attention and perception), and not as a scientific object of interest per se. We claim that the research on video games is in need of a conceptual and methodological framework in which results and effects could be compared, interpreted and generalized. We argue that video games can have multiple effects on players and that these effects can be used as educational potentials. An empirically-based classification of games, depending on their potential effects for an educational purpose, is strongly needed. Likewise, a unified research paradigm and methodologies to carry on reliable research on video games has to be developed.

Number 20064169.

Abstract—Requirements are critical to system validation as they guide all subsequent stages of systems development. Inadequately specified requirements generate systems that require major revisions or cause system failure entirely. Use Cases have become the main vehicle for requirements capture in many current Object Oriented (OO) development methodologies, and a means for developers to communicate with different stakeholders. In this paper we present the results of a laboratory experiment that explored whether different types of use case format are equally effective in facilitating high knowledge user’s understanding. Results showed that the provision of diagrams along with the textual use case descriptions significantly improved user comprehension of system requirements in both familiar and unfamiliar application domains. However, when comparing groups that received models of textual description accompanied with diagrams of different level of details (simple and detailed) we found no significant difference in performance. Keywords—Prior knowledge, Requirement specification, Use case format, User understanding.

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Abstract. This paper presents several methods to automatically detecting students ' mental models in MetaTutor, an intelligent tutoring system that teaches students self-regulatory processes during learning of complex science topics. In particular, we focus on detecting students ' mental models based on studentgenerated paragraphs during prior knowledge activation, a self-regulatory process. We describe two major categories of methods and combine each method with various machine learning algorithms. A detailed comparison among the methods and across all algorithms is also provided. The evaluation of the proposed methods is performed by comparing the prediction of the methods with human judgments on a set of 309 prior knowledge activation paragraphs collected from previous experiments with MetaTutor on college students. According to our experiments, a content-based method with word-weighting and Bayes Nets algorithm is the most accurate. 1