A Framework for Designing Learning Analytics Information Systems

Learning analytics offers new opportunities in higher education, yet the design and development of educational data analytics are facing several challenges. Little guidance is available for researchers and developers when it comes to designing, developing, and implementing learning analytics information systems in higher education. Hence, this study proposes a comprehensive conceptual framework for designing learning analytics information systems incorporating both computational and educational aspects. The framework provides systematic support for learning analytics researchers and designers. It is constructed based on the process and critical dimensions of learning analytics and instructional systems design. By applying the framework to analyze a previously published study, we provide a better understanding of its key qualities. Furthermore, the application of the framework to design a new learning analytics information system provides forward engineering support

TOPICAL EXPRESSIVITY IN SHORT TEXTS

With each passing minute, online data is growing exponentially. A bulk of such data is generated from short text social media platforms such as Twitter. Such platforms are fundamental in social media knowledge-based applications like recommender systems. Twitter, for example, provides rich real-time streaming information. Extracting knowledge from such short texts without automated support is not feasible due to Twitter\u27s platform streaming nature. Therefore, an automated method for comprehending patterns in such text is a need for many knowledge systems. This paper provides solutions to generate topics from Twitter data. We present several techniques related to topical modelling to identify topics of interest in short texts. Topic modelling is inherently problematic in shorter texts with very sparse vocabulary in addition to the informal language used in their dissemination. Such findings are informative in knowledge extraction for social media-based recommender systems as well as in understanding tweeters over time

Follow-back Recommendations for Sports Bettors: A Twitter-based Approach

Social network based recommender systems are powered by a complex web of social discussions and user connections. Short text microblogs e.g. Twitter present powerful frameworks for information consumption, due to their real-time nature in content throughput as well as user connections. Therefore, users on such platforms consume the disseminated content to a greater or lesser extent based on their interests. Quantifying this degree of interest is a difficult task based on the amount of information that such platforms generate at any given time. Thus, the generation of personalized profiles based on the Degree of Interest (DoI) that users have towards certain topics in such short texts presents a research problem. We address this challenge by following a two-step process in generation of personalized sports betting related user profiles in tweets as a case study. We (i) compute the Degree of Interest in Sports Betting (DoiSB) of tweeters and (ii) affirm this DoiSB by correlating it with their friendship network. This is an integral process in the design of a short text based recommender systems for users to follow i.e follow-back recommendations as well as content-based recommendations relying on the interests of users on such platforms. In this paper, we described the DoiSB computation and follow-back recommendation process by building a vector representation model for tweets. We then use this model to profile users interested in sports betting. Experiments using real Twitter dataset geolocated to Kenya shows the effectiveness of our approach in the identification of tweeter\u27s DoiSBs as well as their correlation with their friendship network

A framework for designing learning analytics information systems

Abstract Learning analytics offers new opportunities in higher education, yet the design and development of educational data analytics are facing several challenges. Little guidance is available for researchers and developers when it comes to designing, developing, and implementing learning analytics information systems in higher education. Hence, this study proposes a comprehensive conceptual framework for designing learning analytics information systems incorporating both computational and educational aspects. The framework provides systematic support for learning analytics researchers and designers. It is constructed based on the process and critical dimensions of learning analytics and instructional systems design. By applying the framework to analyze a previously published study, we provide a better understanding of its key qualities. Furthermore, the application of the framework to design a new learning analytics information system provides forward engineering support