Process Support for Learning Tasks in Multimedia Practicals.

Nadolski, R. J. (2004). Process Support for Learning Tasks in Multimedia Practicals. Unpublished doctoral thesis. Open University of the Netherlands, The Netherlands

Implementing E-learning Specifications with Conformance Testing: Profiling for IMS Learning Design

Submitted for publication. Please contact authors for reference.Improving interoperability between e-learning systems and content has been one of the driving forces behind the adoption of e-learning specifications over recent years. A vital step towards achieving this goal is the widespread adoption of conformant implementations of e-learning specifications. A conformant implementation is one which fully complies with the conformance requirements of the specification. However, conformance testing is time consuming and expensive. The process of localising specifications to create so-called “Application Profiles” to meet individual community needs further complicates conformance testing efforts. To solve this problem, we developed the conformance testing approach presented in this article. This approach simplifies the development of Application Profiles, and the process of conformance testing against them. Using this approach, test suites can be generated to test software applications against both e-learning specifications and their derived Application Profiles. A case study based around the IMS Learning Design specification demonstrates this process

Retrospective cognitive feedback for progress monitoring in serious games

Although the importance of cognitive feedback in digital serious games (DSG) is undisputed, we are facing some major design challenges. First of all, we do not know to which extend existing research guidelines apply when we stand the risk of cognitive feedback distorting the delicate balance between learning and playing. Unobtrusive cognitive feedback has to be interspersed with gameplay. Secondly, many effective solutions for providing cognitive feedback we do know might simply be too costly. To face both challenges, this study offers an efficient approach for providing unobtrusive and retrospective cognitive feedback in DSG. This approach was applied onto a game where feedback messages were triggered via simple rules about learners’ questioning behaviour on four dimensions. We found the experimental condition including such retrospective cognitive feedback (RCF) to yield better learning outcomes while maintaining similar motivation. Keyword

Data Fusion for Real-time Multimodal Emotion Recognition through Webcams and Microphones in E-Learning

The original article is available on the Taylor & Francis Online website in the following link: http://www.tandfonline.com/doi/abs/10.1080/10447318.2016.1159799?journalCode=hihc20This paper describes the validation study of our software that uses combined webcam and microphone data for real-time, continuous, unobtrusive emotion recognition as part of our FILTWAM framework. FILTWAM aims at deploying a real time multimodal emotion recognition method for providing more adequate feedback to the learners through an online communication skills training. Herein, timely feedback is needed that reflects on their shown intended emotions and which is also useful to increase learners’ awareness of their own behaviour. At least, a reliable and valid software interpretation of performed face and voice emotions is needed to warrant such adequate feedback. This validation study therefore calibrates our software. The study uses a multimodal fusion method. Twelve test persons performed computer-based tasks in which they were asked to mimic specific facial and vocal emotions. All test persons’ behaviour was recorded on video and two raters independently scored the showed emotions, which were contrasted with the software recognition outcomes. A hybrid method for multimodal fusion of our multimodal software shows accuracy between 96.1% and 98.6% for the best-chosen WEKA classifiers over predicted emotions. The software fulfils its requirements of real-time data interpretation and reliable results.The Netherlands Laboratory for Lifelong Learning (NELLL) of the Open University Netherlands

Multimodal Emotion Recognition for Assessment of Learning in a Game-Based Communication Skills Training

This paper describes how our FILTWAM software artifacts for face and voice emotion recognition will be used for assessing learners' progress and providing adequate feedback in an online game-based communication skills training. This constitutes an example of in-game assessment for mainly formative purposes. During this training, learners are requested to mimic specific emotions via a webcam and a microphone in which the software artifacts determine the adequacy of the mimicked emotion from either face and/or voice. Our previous studies have shown that these software artifacts are able to detect face and voice emotions in real-time and with sufficient reliability. In our current work, we present a software system architecture that unobtrusively monitors learners’ behaviors in an online game- based approach and offers timely and relevant feedback based upon learner’s face and voice expressions. Whereas emotion detection is often used for adapting learning content or learning tasks, our approach focuses on using emotions for guiding learners towards improved communication skills. Herein, learners need to have an opportunity of frequent guided practice in order to learn how to express the right emotion at the right time. We assume that this approach can address several issues with the current trainings in this area. We sketch the research design of our planned study that investigates the efficiency, effectiveness and enjoyableness of our approach. We conclude the paper by considering the challenges of this study.The Netherlands Laboratory for Lifelong Learning (NELLL) of the Open University of the Netherlands

Communication skills training exploiting multimodal emotion recognition

The teaching of communication skills is a labour-intensive task because of the detailed feedback that should be given to learners during their prolonged practice. This study investigates to what extent our FILTWAM facial and vocal emotion recognition software can be used for improving a serious game (the Communication Advisor) that delivers a web-based training of communication skills. A test group of 25 participants played the game wherein they were requested to mimic specific facial and vocal emotions. Half of the assignments included direct feedback and the other half included no feedback. It was investigated whether feedback on the mimicked emotions would lead to better learning. The results suggest the facial performance growth was found to be positive, particularly significant in the feedback condition. The vocal performance growth was significant in both conditions. The results are a significant indication that the automated feedback from the software improves learners’ communication performances.The Netherlands Laboratory for Lifelong Learning (NELLL) of the Open University Netherland

Serious Gaming Analytics: What Students´ Log Files Tell Us about Gaming and Learning

In this paper we explore existing log files of the VIBOA environmental policy game. Our aim is to identify relevant player behaviours and performance patterns. The VIBOA game is a 50 hours master level serious game that supports inquiry-based learning: students adopt the role of an environmental consultant in the (fictitious) consultancy agency VIBOA, and have to deal with complex, multi-faceted environmental problems in an academic and methodologically sound way. A sample of 118 master students played the game. We used learning analytics to extract relevant data from the logging and find meaningful patterns and relationships. We observed substantial behavioural variability across students. Correlation analysis suggest a behavioural trade that reflects the rate of “switching” between different game objects or activities. We were able to establish a model that uses switching indicators as predictors for the efficiency of learning. Also we found slight evidence that students who display increased switching behaviours need more time to complete the games. We conclude the paper by critically evaluating our findings, making explicit the limitations of our study and making suggestions for future research that links together learning analytics and serious gaming

AD39 Learning Design UML Profile

This document shows the UML Profile modelled for Learning Design, so it can be used as input for the Telcert Test System. The focus is not so much on the Learning Design as on the modelling of it