A digital ecosystems model of assessment feedback on student learning

The term ecosystem has been used to describe complex interactions between living organisms and the physical world. The principles underlying ecosystems can also be applied to complex human interactions in the digital world. As internet technologies make an increasing contribution to teaching and learning practice in higher education, the principles of digital ecosystems may help us understand how to maximise technology to benefit active, self-regulated learning especially among groups of learners. Here, feedback on student learning is presented within a conceptual digital ecosystems model of learning. Additionally, we have developed a Web 2.0-based system, called ASSET, which incorporates multimedia and social networking features to deliver assessment feedback within the functionality of the digital ecosystems model. Both the digital ecosystems model and the ASSET system are described and their implications for enhancing feedback on student learning are discussed.

Space partitioning for scalable K-means

K-Means is a popular clustering algorithm which adopts an iterative refinement procedure to determine data partitions and to compute their associated centres of mass, called centroids. The straightforward implementation of the algorithm is often referred to as `brute force' since it computes a proximity measure from each data point to each centroid at every iteration of the K-Means process. Efficient implementations of the K-Means algorithm have been predominantly based on multi-dimensional binary search trees (KD-Trees). A combination of an efficient data structure and geometrical constraints allow to reduce the number of distance computations required at each iteration. In this work we present a general space partitioning approach for improving the efficiency and the scalability of the K-Means algorithm. We propose to adopt approximate hierarchical clustering methods to generate binary space partitioning trees in contrast to KD-Trees. In the experimental analysis, we have tested the performance of the proposed Binary Space Partitioning K-Means (BSP-KM) when a divisive clustering algorithm is used. We have carried out extensive experimental tests to compare the proposed approach to the one based on KD-Trees (KD-KM) in a wide range of the parameters space. BSP-KM is more scalable than KDKM, while keeping the deterministic nature of the `brute force' algorithm. In particular, the proposed space partitioning approach has shown to overcome the well-known limitation of KD-Trees in high-dimensional spaces and can also be adopted to improve the efficiency of other algorithms in which KD-Trees have been used.

Moving feedback forward: theory to practice

There is substantial research interest in tutor feedback and students’ perception and use of such feedback. This paper considers some of the major issues raised in relation to tutor feedback and student learning. We explore some of the current feedback drivers, most notably the need for feedback to move away from simply a monologue from a tutor to a student to a valuable tutor–student dialogue. In relation to moving feedback forward the notions of self regulation, dialogue and social learning are explored and then considered in relation to how such theory can translate into practice. The paper proposes a framework (GOALS) as a tool through which tutors can move theory into practice with the aim of improving student learning from feedback.

ASSET: moving forward through feedback

Providing high quality and timely feedback to students is often a challenge for many staff in higher education as it can be both time-consuming and frustratingly repetitive. From the student perspective, feedback may sometimes be considered unhelpful, confusing and inconsistent and may not always be provided within a timeframe that is considered to be ‘useful’. The ASSET project, based at the University of Reading, addresses many of these inherent challenges by encouraging the provision of feedback that supports learning, i.e. feedback that contains elements of ‘feed-forward’, is of a high quality and is delivered in a timely manner. In particular, the project exploits the pedagogic benefits of video/audio media within a Web 2.0 context to provide a new, interactive resource, ‘ASSET’, to enhance the feedback experience for both students and staff. A preliminary analysis of both our quantitative and qualitative pedagogic data demonstrate that the ASSET project has instigated change in the ways in which both staff and students think about, deliver, and engage with feedback. For example, data from our online questionnaires and focus groups with staff and students indicate a positive response to the use of video as a medium for delivering feedback to students. In particular, the academic staff engaged in piloting the ASSET resource indicated that i) using video has made them think more, and in some cases differently, about the ways in which they deliver feedback to students and ii) they now see video as an effective means of making feedback more useful and engaging for students. Moreover, the majority of academic staff involved in the project have said they will continue to use video feedback. From the student perspective, 60% of those students whose lecturers used ASSET to provide video feedback said that “receiving video feedback encouraged me to take more notice of the feedback compared with normal methods” and 80% would like their lecturer to continue to use video as a method for providing feedback. An important aim of the project was for it to complement existing University-wide initiatives on feedback and for ASSET to become a ‘model’ resource for staff and students wishing to explore video as a medium for feedback provision. An institutional approach was therefore adopted and key members of Senior Management, academics, T&L support staff, IT support and Student Representatives were embedded within the project from the start. As with all initiatives of this kind, a major issue is the future sustainability of the ASSET resource and to have had both ‘top-down’ and ‘bottom-up’ support for the project has been extremely beneficial. In association with the project team the University is currently exploring the creation of an open-source, two-tiered video supply solution and a ‘framework’ (that other HEIs can adopt and/or adapt) to support staff in using video for feedback provision. In this way students and staff will have new opportunities to explore video and to exploit the benefits of this medium for supporting learning.

PDM 2006: Proceedings of the International Workshop on Data Mining

Recently major processor manufacturers have announced a dramatic shift in their paradigm to increase computing power over the coming years. Instead of focusing on faster clock speeds and more powerful single core CPUs, the trend clearly goes towards multi core systems. This will also result in a paradigm shift for the development of algorithms for computationally expensive tasks, such as data mining applications. Obviously, work on parallel algorithms is not new per se but concentrated efforts in the many application domains are still missing. Multi-core systems, but also clusters of workstations and even large-scale distributed computing infrastructures provide new opportunities and pose new challenges for the design of parallel and distributed algorithms. Since data mining and machine learning systems rely on high performance computing systems, research on the corresponding algorithms must be on the forefront of parallel algorithm research in order to keep pushing data mining and machine learning applications to be more powerful and, especially for the former, interactive. To bring together researchers and practitioners working in this exciting field, a workshop on parallel data mining was organized as part of PKDD/ECML 2006 (Berlin, Germany). The six contributions selected for the program describe various aspects of data mining and machine learning approaches featuring low to high degrees of parallelism: The first contribution focuses the classic problem of distributed association rule mining and focuses on communication efficiency to improve the state of the art. After this a parallelization technique for speeding up decision tree construction by means of thread-level parallelism for shared memory systems is presented. The next paper discusses the design of a parallel approach for dis- tributed memory systems of the frequent subgraphs mining problem. This approach is based on a hierarchical communication topology to solve issues related to multi-domain computational envi- ronments. The forth paper describes the combined use and the customization of software packages to facilitate a top down parallelism in the tuning of Support Vector Machines (SVM) and the next contribution presents an interesting idea concerning parallel training of Conditional Random Fields (CRFs) and motivates their use in labeling sequential data. The last contribution finally focuses on very efficient feature selection. It describes a parallel algorithm for feature selection from random subsets. Selecting the papers included in this volume would not have been possible without the help of an international Program Committee that has provided detailed reviews for each paper. We would like to also thank Matthew Otey who helped with publicity for the workshop.

K-Surfer: a KNIME extension for the management and analysis of Human brain MRI FreeSurfer/FSL data

Human brain imaging techniques, such as Magnetic Resonance Imaging (MRI) or Diffusion Tensor Imaging (DTI), have been established as scientific and diagnostic tools and their adoption is growing in popularity. Statistical methods, machine learning and data mining algorithms have successfully been adopted to extract predictive and descriptive models from neuroimage data. However, the knowledge discovery process typically requires also the adoption of pre-processing, post-processing and visualisation techniques in complex data workflows. Currently, a main problem for the integrated preprocessing and mining of MRI data is the lack of comprehensive platforms able to avoid the manual invocation of preprocessing and mining tools, that yields to an error-prone and inefficient process. In this work we present K-Surfer, a novel plug-in of the Konstanz Information Miner (KNIME) workbench, that automatizes the preprocessing of brain images and leverages the mining capabilities of KNIME in an integrated way. K-Surfer supports the importing, filtering, merging and pre-processing of neuroimage data from FreeSurfer, a tool for human brain MRI feature extraction and interpretation. K-Surfer automatizes the steps for importing FreeSurfer data, reducing time costs, eliminating human errors and enabling the design of complex analytics workflow for neuroimage data by leveraging the rich functionalities available in the KNIME workbench.