Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Organizational learning II: Theory, method and practice

Matrix Factorization Techniques for Recommender Systems

Recently, there is an increasing interest in learning analytics in Technology-Enhanced Learning TEL. Generally, learning analytics deals with the development of methods that harness educational datasets to support the learning process. Learning analytics LA is a multi-disciplinary field involving machine learning, artificial intelligence, information retrieval, statistics and visualisation. LA is also a field in which several related areas of research in TEL converge. These include academic analytics, action analytics and educational data mining. In this paper, we investigate the connections between LA and these related fields. We describe a reference model for LA based on four dimensions, namely data and environments what?, stakeholders who?, objectives why? and methods how?. We then review recent publications on LA and its related fields and map them to the four dimensions of the reference model. Furthermore, we identify various challenges and research opportunities in the area of LA in relation to each dimension.

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A reference model for learning analytics

eleed, Iss. 10 - In recent years, learning analytics (LA) has attracted a great deal of attention in technology-enhanced learning (TEL) research as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future TEL landscape. Generally, LA deals with the development of methods that harness educational data sets to support the learning process. This paper provides a foundation for future research in LA. It provides a systematic overview on this emerging field and its key concepts through a reference model for LA based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). It further identifies various challenges and research opportunities in the area of LA in relation to each dimension.

Learning Analytics: Challenges and Future Research Directions

The personal learning environment (PLE) concept offers a learner-centric view of learning and suggests a shift from knowledge-push to knowledge-pull approach to learning. One concern with a PLE-driven knowledge-pull approach to learning, however, is information overload. Recommender systems can provide an effective mechanism to deal with the information overload problem in PLEs. In this paper, we study different tag-based collaborative filtering recommendation techniques on their applicability and effectiveness in PLE settings. We implement 16 different tag-based collaborative filtering recommendation algorithms, memory based as well as model based, and compare them in terms of accuracy and user satisfaction. The results of the conducted offline and user evaluations reveal that the quality of user experience does not correlate with high-recommendation accuracy.

Tag-Based Collaborative Filtering Recommendation in Personal Learning Environments

The widespread use of mobile technologies has led to an increasing interest in mobile learning. Context is a central topic of research in that area. In fact, a major benefit of mobile devices is that they enable learning across contexts. In this paper, we explore how context can deliver significant benefits in mobile learning and provide an extensive review of the current literature and research on mobile learning in context. Furthermore, we identify various challenges and research opportunities in this area and propose the conceptual framework CAMeL for context-aware mobile learning.

Mobile learning in context

There are many different ways and models how to characterize usage data to enable representation of user actions across learning management system, and systems in general. Based on this data, learning analytics can perform different analysis and provide personalized and meaningful information to improve the learning and teaching processes. There is a variety of usage data formats that are already successfully used in exiting systems. These different usage data formats have their advantages and disadvantage that have to be considered when using them in the context of learning analytics. In this paper, several usage data formats are presented and analyzed in the context of learning analytics to help in choosing the best suiting usage data model.

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Hendrik Thüs

Papers

A reference model for learning analytics

Learning Analytics: Challenges and Future Research Directions

Tag-Based Collaborative Filtering Recommendation in Personal Learning Environments

Mobile learning in context

Data Models in Learning Analytics