Evaluation of e-learning web sites using fuzzy axiomatic design based approach

High quality web site has been generally recognized as a critical enabler to conduct online business. Numerous studies exist in the literature to measure the business performance in relation to web site quality. In this paper, an axiomatic design based approach for fuzzy group decision making is adopted to evaluate the quality of e-learning web sites. Another multi-criteria decision making technique, namely fuzzy TOPSIS, is applied in order to validate the outcome. The methodology proposed in this paper has the advantage of incorporating requirements and enabling reductions in the problem size, as compared to fuzzy TOPSIS. A case study focusing on Turkish e-learning websites is presented, and based on the empirical findings, managerial implications and recommendations for future research are offered

Educational Implications of CELIA : Learning by Observing and Explaining

CELIA is a computational model of how a novice student can quickly become competent at a procedural task through observing and understanding an expert's problem solving. This model was inspired by protocol studies, and implemented in a computer program. This model of a student's effective learning suggests some implications for teaching novices in a new domain. These may be relevant for both human teaching and intelligent tutoring. The implications include: encourage the student to predict, interactive step-by-step presentation of example steps, encourage self-explanation by the student, order example steps to match their logical order, give a variety of examples in early instruction, allow flexible interaction with the student, and present bztsic background concepts prior to examples. These implications represent hypotheses that follow from the learning model; they suggest further research

Improving Natural Language Inference Using External Knowledge in the Science Questions Domain

Natural Language Inference (NLI) is fundamental to many Natural Language Processing (NLP) applications including semantic search and question answering. The NLI problem has gained significant attention thanks to the release of large scale, challenging datasets. Present approaches to the problem largely focus on learning-based methods that use only textual information in order to classify whether a given premise entails, contradicts, or is neutral with respect to a given hypothesis. Surprisingly, the use of methods based on structured knowledge -- a central topic in artificial intelligence -- has not received much attention vis-a-vis the NLI problem. While there are many open knowledge bases that contain various types of reasoning information, their use for NLI has not been well explored. To address this, we present a combination of techniques that harness knowledge graphs to improve performance on the NLI problem in the science questions domain. We present the results of applying our techniques on text, graph, and text-to-graph based models, and discuss implications for the use of external knowledge in solving the NLI problem. Our model achieves the new state-of-the-art performance on the NLI problem over the SciTail science questions dataset.Comment: 9 pages, 3 figures, 5 table

The effect of knowledge management and organisational learning on individual competencies

Knowledge management (KM) is known for its positive impact on the strategy of organisations, but little is known and understood about the significance of competency and learning and its important effects on knowledge management in public and private organisations in different sectors of the economy in Kuwait. The problem is that many organisations deal with KM or new information or emerging information as a challenge of KM itself rather than a way of incorporating new knowledge into the organisation through the development of individual competencies, and hence developing both KM and individual competency. Based on interview data from Kuwaiti organisations, this paper argues that it is better to implement KM and maximize organizational learning in order to create more competent individuals based on the spiral of knowledge creation model or the theory of knowledge creation. The significant contribution this paper makes is that individual competencies have a reciprocal relationship with KM; the determining factors of individual competencies training, education, personal characteristics and culture affect KM success and are themselves affected by KM strategies. Some implications for managing organisational knowledge, organisational learning and development of individual competency are considered

Distributed Low-rank Subspace Segmentation

Vision problems ranging from image clustering to motion segmentation to semi-supervised learning can naturally be framed as subspace segmentation problems, in which one aims to recover multiple low-dimensional subspaces from noisy and corrupted input data. Low-Rank Representation (LRR), a convex formulation of the subspace segmentation problem, is provably and empirically accurate on small problems but does not scale to the massive sizes of modern vision datasets. Moreover, past work aimed at scaling up low-rank matrix factorization is not applicable to LRR given its non-decomposable constraints. In this work, we propose a novel divide-and-conquer algorithm for large-scale subspace segmentation that can cope with LRR's non-decomposable constraints and maintains LRR's strong recovery guarantees. This has immediate implications for the scalability of subspace segmentation, which we demonstrate on a benchmark face recognition dataset and in simulations. We then introduce novel applications of LRR-based subspace segmentation to large-scale semi-supervised learning for multimedia event detection, concept detection, and image tagging. In each case, we obtain state-of-the-art results and order-of-magnitude speed ups

Investigating Variability in Teaching Performance...Seeking Pathways to Excellence

Teacher learning is critical to student learning (Darling-Hammond, 2002, 2010). The work documented here is driven by an investigation of a long-standing and complex problem of educational practice: the inequitable learning opportunities for students that result from variability in the selection, learning and placement of practicing and aspiring teachers. A multidisciplinary perspective is used to situate the problem of practice theoretically, within a body of empirical research, and within a context of educational practice. Among the perspectives used to examine the problem of practice are theoretical frameworks that support the claim that the problem is a matter of social justice. The investigation also argues that inequitable learning opportunities for students are impacted by a fusion of two critical factors including the avenues by which people are recruited for and granted access to teacher preparation programs and the structure and quality of professional development provided to practicing teachers. The argument acknowledges the concept of variability within systems and practices, but contends that variability within excellence is the environment that will afford quality teachers for all students. Efforts to understand and address the problem are addressed to reveal what has been learned in the investigation to date and how what needs to be learned will form a leadership agenda that engages a diversity of stakeholders collaborating on an effort to improve an educational system in which the problem of practice exists. The implications of the effort are discussed for individuals, for the system, and with regard to leadership issues that bear on the problem of practice. The work concludes with a summary of what has been learned through the investigation and the implications of that learning for the professional leadership agenda that will be pursued in order to establish collaboratively engaged improvement efforts as a norm of practice at the level of schools and school districts