Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

Learning from Internal Change Academy processes

A 2007/8 Research and Development Grant from SEDA under its Supporting and Leading Educational Change programme provided Sheffield Hallam University with the opportunity to undertake an extremely interesting and timely piece of work on learning from Internal Change Academy processes. A presentation to the SEDA Spring Conference 2009 focused on understanding the value of Internal Change Academies as a model for leading educational change and demonstrated how a simple benchmarking exercise may provide a rich source of data in leading change processes. This article focuses on the broader lessons learnt about change rather than on the practicalities and the different models of running an Internal Change Academy. That information is available in the project final report (Flint and Oxley, 2009)

Learning Markov Decision Processes for Model Checking

Constructing an accurate system model for formal model verification can be both resource demanding and time-consuming. To alleviate this shortcoming, algorithms have been proposed for automatically learning system models based on observed system behaviors. In this paper we extend the algorithm on learning probabilistic automata to reactive systems, where the observed system behavior is in the form of alternating sequences of inputs and outputs. We propose an algorithm for automatically learning a deterministic labeled Markov decision process model from the observed behavior of a reactive system. The proposed learning algorithm is adapted from algorithms for learning deterministic probabilistic finite automata, and extended to include both probabilistic and nondeterministic transitions. The algorithm is empirically analyzed and evaluated by learning system models of slot machines. The evaluation is performed by analyzing the probabilistic linear temporal logic properties of the system as well as by analyzing the schedulers, in particular the optimal schedulers, induced by the learned models.Comment: In Proceedings QFM 2012, arXiv:1212.345

Organizational learning processes in downsizing

The purpose of this article is to explore organizational learning processes by examining how companies learn to do something new, namely downsize, on the basis of a small sample of companies in Europe. In a first step, the range of possible responses to downsizing, as discussed in the literature, is presented and compared with the responses described in the interviews. Significant gaps between the options described in the literature and the activities undertaken by the sample companies were found. In a second step, models of organizational learning are presented, specifically focusing on knowledge acquisition, information distribution and interpretation, and compared with the learning processes described by the sample companies. The article suggests that had the companies used a broader range of knowledge acquisition strategies, they might have expanded their range of responses to downsizing. A revision of organizational learning models to include a greater variety of perspectives in a problem definition phase before knowledge acquisition is undertaken is recommended. -- Um erfassen zu können, wie europäische Firmen lernen, die für sie neuen Aufgaben des Downsizing zu planen und durchzuführen, wurden zunächst die Erfahrungen in der US-amerikanischen Wirtschaft anhand einer Literaturanalyse und durch Sekundärstudien ausgewählter Untersuchungen ausgewertet. Anschließend wurde eine Primärerhebung durchgeführt. Es wurden Expertengespräche in 13 Unternehmen aus unterschiedlichen Branchen in vier westeuropäischen Ländern (vorrangig in Deutschland) durchgeführt. Die Auswertung der Interviews mit Führungskräften zeigt, daß in Europa die Erfahrungen der USA bisher so gut wie nicht wahrgenommen worden sind. Dieser Beitrag stellt die Lernstrategien der untersuchten Firmen dar und zeigt die Lücken auf, die sowohl für die Praxis wie auch für die Theoriebildung im Bereich Organisationslernen entstehen.