Learning Analytics for Learning Design: Towards Evidence-Driven Decisions to Enhance Learning

As the fields of learning analytics and learning design mature, the convergence and synergies between them become an important area for research. Collecting and combining learning analytics coming from different channels can clearly provide valuable information in designing learning. Hence, this paper intends to summarize the main outcomes of a systematic literature review of empirical evidence on learning analytics for learning design. The search was performed in seven academic databases, resulting in 38 papers included in the main analysis. The review demonstrates ongoing design patterns and learning phenomena that improve learning, by providing more comprehensive background of the current landscape of learning analytics for learning design and its impact on the current status of learning technologies. Consequently, future research should consider how to capture and systematize learning design data. Moreover, it should evaluate and document what learning design choices made by educators using what learning analytics techniques influence learning experiences and learning performances over time

Evolvable Media Repositories: An Evolutionary System to Retrieve and Ever-Renovate Related Media Web Content

The paper tackles the question of evolvable media reposito- ries, i.e., local pools of media files that are retrieved over the Internet and that are ever-renovated with new, related files in an evolutionary fash- ion. The herein proposed method encodes genotypic space by virtue of simple undirected graphs of natural language tokens that represent web queries without employing fitness functions or other evaluation/selection schemata. Once a first population is seeded, a series of modular crawlers query the particular World Wide Web repositories of interest for both media content and assorted meta-data. Then, a series of attached intelli- gent comprehenders analyse the retrieved content in order to eventually generate new genetic representations, and the cycle is repeated. Such a method is generic, scalable and modular, and can be made fit the pur- poses of a wide array of applications in all sorts of disparate contextual and functional scenarios. The paper features a formal description of the method, gives implementation guidelines, and presents example usages