Martin Muehlenbrock

Bio: Martin Muehlenbrock is an academic researcher from German Research Centre for Artificial Intelligence. The author has contributed to research in topics: Collaborative learning & Educational technology. The author has an hindex of 4, co-authored 5 publications receiving 729 citations.

From Mirroring to Guiding: A Review of State of the Art Technology for Supporting Collaborative Learning

Abstract: We review a representative selection of systems that support the management of collaborative learning interaction, and characterize them within a simple classification framework. The framework distinguishes between mirroring systems, which display basic actions to collaborators, metacognitive tools, which represent the state of interaction via a set of key indicators, and coaching systems, which offer advice based on an interpretation of those indicators. The reviewed systems are further characterized by the type of interaction data they assimilate, the processes they use for deriving higher-level data representations, the variables or indicators that characterize these representations, and the type of feedback they provide to students and teachers. This overview of technological capabilities is designed to lay the groundwork for further research into which technological solutions are appropriate for which learning situations.

Automatic Action Analysis in an Interactive Learning Environment

Abstract: Recently, there is a growing interest in the automatic analysis of learner interaction data with web-based learning environments. The approach presented in this paper aims at helping to establish a basis for the automatic analysis of interaction data by developing a data logging and analysis system based on a standard data base server and standard machine learning techniques. The analysis system has been connected to a web-based interactive learning environment for mathematics teaching, but is designed to allow for interfacing also to other web based learning environments. The system has been tested in a five-month experiment in which four classes of a secondary school participated throughout a complete school term on a weekly basis. Recently, there is a growing interest in the automatic analysis of learner interaction data with web-based learning environments. This is largely due to the increasing availability of log data from learning environments and in particular from web-based ones. The objectives include the detection of regularities and deviations in the learners' or teachers' actions among others, and to support teachers and learners by providing them with additional information to mange their learning and teaching, respectively, and possibly suggest remedial actions. Commercial systems such as WebCT, Blackboard, and LearningSpace already give access to some information related to the activity of the learners including some statistical analyses, and provide teachers with information on course attendance and exam results. With this information already being useful, it only represents the tip of iceberg of what might be possible by using advanced technologies. This upcoming field, i.e., addressing the automatic analysis of learner interaction data, is related to several well-established areas of research including intelligent tutoring systems, web mining, and machine learning, and can build upon results form these fields for achieving its objectives. In contrast to intelligent tutoring systems, learner interaction analysis does not rely on models of learner or domain knowledge since these are heavy to build and maintain. In this regards, learner interaction analysis is comparable to website data mining, but with a specific perspective on learning settings and with the availability of pedagogical data that usually are not available in web mining applications that are mostly based on click through data. Click through data streams only allow for a rather shallow analysis, but with the inclusion of pedagogical data also more advanced techniques can be adopted from the field of machine learning.

Learning Group Formation Based on Learner Profile and Context

Abstract: An important but often neglected aspect in Computer-Supported Collaborative Learning (CSCL) is the formation of learning groups. Until recently, most support for group formation was based on learner profile information. In addition, the perspective of ubiquitous computing and ambient intelligence allows for a wider perspective on group formation, broadening the range of addressed features to include learner context information.

Formation of Learning Groups by using Learner Profiles and Context Information

Abstract: An important but often neglected aspect in Computer Supported Collaborative Learning is the intelligent formation of learning groups Until recently, support for group formation was mostly based on learner profile information However, the perspective of ubiquitous computing and ambient intelligence allows for taking a broader view on group formation, extending the range of features to include learner context information such as sensor-derived activity and availability A probabilistic approach has been developed that automatically learns individual characteristics and indicates relevant situations, and which has been tested in a set of experiments

Workshop on Designing Computational Models of Collaborative Learning Interaction

Abstract: During collaborative learning activities, factors such as students’ prior knowledge, motivation,roles, language, behavior and interaction dynamics interact with each other in unpredictable ways, making it very difficult to predict and measure learning effects. This may be one reason why the focus of collaborative learning research shifted in the nineties from studying group characteristics and products to studying group process. With an interest in having an impact on the group process in modern distance learning environments, the focus has recently shifted again – this time from studying group processes to identifying computational strategies that positively influence group learning. This shift toward mediating and supporting collaborative learners is fundamentally grounded in our understanding of the interaction described by our models of collaborative learning interaction. In this workshop, we will explore the advantages, implications, and support possibilities afforded by the various types of computational models of collaborative learning processes.

Educational Data Mining: A Review of the State of the Art

Abstract: Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

Educational data mining: A survey from 1995 to 2005

Abstract: Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. Each of these systems has different data source and objectives for knowledge discovering. After preprocessing the available data in each case, data mining techniques can be applied: statistics and visualization; clustering, classification and outlier detection; association rule mining and pattern mining; and text mining. The success of the plentiful work needs much more specialized work in order for educational data mining to become a mature area.

Data mining in course management systems: Moodle case study and tutorial

Abstract: Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. This work is a survey of the specific application of data mining in learning management systems and a case study tutorial with the Moodle system. Our objective is to introduce it both theoretically and practically to all users interested in this new research area, and in particular to online instructors and e-learning administrators. We describe the full process for mining e-learning data step by step as well as how to apply the main data mining techniques used, such as statistics, visualization, classification, clustering and association rule mining of Moodle data. We have used free data mining tools so that any user can immediately begin to apply data mining without having to purchase a commercial tool or program a specific personalized tool.

The Evolution of Research on Computer-Supported Collaborative Learning

Abstract: This chapter summarizes two decades of research on computer-supported collaborative learning (CSCL). We first review the key idea that has emerged, namely the fact that collaboration among peers can be “designed”, that is, directly or indirectly shaped by the CSCL environment. Second, we stress the fact that affective and motivational aspects that influence collaborative learning have been neglected by experimental CSCL researchers. Finally, we point out the emergence of a new trend or new challenge: integration of CSCL activities into larger pedagogical scenarios that include multiple activities and must be orchestrated in real time by the teacher.

Building Intelligent Interactive Tutors: Student-centered Strategies for Revolutionizing E-learning

Abstract: Computers have transformed every facet of our culture, most dramatically communication, transportation, finance, science, and the economy. Yet their impact has not been generally felt in education due to lack of hardware, teacher training, and sophisticated software. Another reason is that current instructional software is neither truly responsive to student needs nor flexible enough to emulate teaching. The more instructional software can reason about its own teaching process, know what it is teaching, and which method to use for teaching, the greater is its impact on education. Building Intelligent Interactive Tutors discusses educational systems that assess a student's knowledge and are adaptive to a student's learning needs. Dr. Woolf taps into 20 years of research on intelligent tutors to bring designers and developers a broad range of issues and methods that produce the best intelligent learning environments possible, whether for classroom or life-long learning. The book describes multidisciplinary approaches to using computers for teaching, reports on research, development, and real-world experiences, and discusses intelligent tutors, web-based learning systems, adaptive learning systems, intelligent agents and intelligent multimedia. *Combines both theory and practice to offer most in-depth and up-to-date treatment of intelligent tutoring systems available *Presents powerful drivers of virtual teaching systems, including cognitive science, artificial intelligence, and the Internet *Features algorithmic material that enables programmers and researchers to design building components and intelligent systems