END USER LEARNING BEHAVIOR IN DATA ANALYSIS AND DATA MODELING TOOLS

The research examined naive user analysts\u27 learning of data analysis skills; namely. (1) the difficulty of learning data analysis, (2) the differential learning rates among development tools, and (3) the dimensions of the tools contributing to the learning differences. A total of fifty-six students participated in two experiments. The experiments involved repeaied trials of practice and feedback in drawing application-based data models. On average, the participants were experienced end users of computer systems in organizations. The two tools examined in the experiments were the logical data structure model (LDS), which is based on the entity-relationship concept, and the relational data model (RDM). The correctness of the models improved over the trials in both LDS and RDM groups with LDS users performing better than RDM users, particularly in terms of representing relationships. LDS users were found to be more top-down motivated in their method of analysis than RDM users. The study suggests that among end users, the LDS formalism is more easily learned than the RDM formalism. The results also imply that end-user training should stress conceptual top*wn analysis, not bottom-up output directed analysis

ALOJA: A framework for benchmarking and predictive analytics in Hadoop deployments

This article presents the ALOJA project and its analytics tools, which leverages machine learning to interpret Big Data benchmark performance data and tuning. ALOJA is part of a long-term collaboration between BSC and Microsoft to automate the characterization of cost-effectiveness on Big Data deployments, currently focusing on Hadoop. Hadoop presents a complex run-time environment, where costs and performance depend on a large number of configuration choices. The ALOJA project has created an open, vendor-neutral repository, featuring over 40,000 Hadoop job executions and their performance details. The repository is accompanied by a test-bed and tools to deploy and evaluate the cost-effectiveness of different hardware configurations, parameters and Cloud services. Despite early success within ALOJA, a comprehensive study requires automation of modeling procedures to allow an analysis of large and resource-constrained search spaces. The predictive analytics extension, ALOJA-ML, provides an automated system allowing knowledge discovery by modeling environments from observed executions. The resulting models can forecast execution behaviors, predicting execution times for new configurations and hardware choices. That also enables model-based anomaly detection or efficient benchmark guidance by prioritizing executions. In addition, the community can benefit from ALOJA data-sets and framework to improve the design and deployment of Big Data applications.This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). This work is partially supported by the Ministry of Economy of Spain under contracts TIN2012-34557 and 2014SGR1051.Peer ReviewedPostprint (published version

Supporting teachers in collaborative student modeling: a framework and an implementation

Collaborative student modeling in adaptive learning environments allows the learners to inspect and modify their own student models. It is often considered as a collaboration between students and the system to promote learners’ reflection and to collaboratively assess the course. When adaptive learning environments are used in the classroom, teachers act as a guide through the learning process. Thus, they need to monitor students’ interactions in order to understand and evaluate their activities. Although, the knowledge gained through this monitorization can be extremely useful to student modeling, collaboration between teachers and the system to achieve this goal has not been considered in the literature. In this paper we present a framework to support teachers in this task. In order to prove the usefulness of this framework we have implemented and evaluated it in an adaptive web-based educational system called PDinamet.Postprint (author's final draft