A Hybrid Recommender Strategy on an Expanded Content Manager in Formal Learning

The main topic of this paper is to find ways to improve learning in a formal Higher Education Area. In this environment, the teacher publishes or suggests contents that support learners in a given course, as supplement of classroom training. Generally, these materials are pre-stored and not changeable. These contents are typically published in learning management systems (the Moodle platform emerges as one of the main choices) or in sites created and maintained on the web by teachers themselves. These scenarios typically include a specific group of students (class) and a given period of time (semester or school year). Contents reutilization often needs replication and its update requires new edition and new submission by teachers. Normally, these systems do not allow learners to add new materials, or to edit existing ones. The paper presents our motivations, and some related concepts and works. We describe the concepts of sequencing and navigation in adaptive learning systems, followed by a short presentation of some of these systems. We then discuss the effects of social interaction on the learners’ choices. Finally, we refer some more related recommender systems and their applicability in supporting learning. One central idea from our proposal is that we believe that students with the same goals and with similar formal study time can benefit from contents' assessments made by learners that already have completed the same courses and have studied the same contents. We present a model for personalized recommendation of learning activities to learners in a formal learning context that considers two systems. In the extended content management system, learners can add new materials, select materials from teachers and from other learners, evaluate and define the time spent studying them. Based on learner profiles and a hybrid recommendation strategy, combining conditional and collaborative filtering, our second system will predict learning activities scores and offers adaptive and suitable sequencing learning contents to learners. We propose that similarities between learners can be based on their evaluation interests and their recent learning history. The recommender support subsystem aims to assist learners at each step suggesting one suitable ordered list of LOs, by decreasing order of relevance. The proposed model has been implemented in the Moodle Learning Management System (LMS), and we present the system’s architecture and design. We will evaluate it in a real higher education formal course and we intend to present experimental results in the near future

END - A Lightweight Algorithm to Estimate the Number of Defects in Software

Defect precision provides information on how many defects a given software application appears to have. Existing approaches are usually based on time consuming model-based techniques. A viable alternative is the previously presented Abacus algorithm, which is based on Bayesian fault diagnosis. This paper presents a novel alternative approach- coined End- that uses the same input and produces the same output as the Abacus algorithm, but is considerably more time efficient. An experiment was conducted to compare both the accuracy and performance of these two algorithms. The End algorithm presented the same accuracy as the Abacus algorithm, but outperformed it in the majority of executions.

A kernel density estimate-based approach to component goodness modeling

Intermittent fault localization approaches account for the fact that faulty components may fail intermittently by considering a parameter (known as goodness) that quantifies the probability that faulty components may still exhibit correct behavior. Current, state-of-the-art approaches (1) assume that this goodness probability is context independent and (2) do not provide means for integrating past diagnosis experience in the diagnostic mechanism. In this paper, we present a novel approach, coined Non-linear Feedback-based Goodness Estimate (NFGE), that uses kernel density estimations (KDE) to address such limitations. We evaluated the approach with both synthetic and real data, yielding lower estimation errors, thus increasing the diagnosis performance

Enhancing reasoning approaches to diagnose functional and non-functional errors

Most approaches to automatic software diagnosis abstract the system under analysis in terms of component activity and correct/incorrect behaviour (colectivelly known as spectra). While this binary error abstraction has been shown to be capable of diagnosing functional errors, when diagnosing non-functional errors it yields suboptimal accuracy. The main reason for this limitation is related to the lack of mechanisms for encoding error symptoms (such as performance degradation) in such a binary schema. In this paper, we propose a novel approach to diagnose both functional and non-functional errors by incorporating into the classic, bayesian reasoning approaches to error diagnosis concepts from the fuzzy logic domain. The empirical evaluation on 27000 synthetic scenarios demonstrates that the proposed fuzzy logic-based approach considerably improves the diagnostic accuracy (20% on average, with 99% statistical significance) when compared to the classic, state-of-the-art approach

An efficient distributed algorithm for computing minimal hitting sets

Computing minimal hitting sets for a collection of sets is an important problem in many domains (e.g., Spectrum-based Fault Localization). Being an NP-Hard problem, exhaustive algorithms are usually prohibitive for real-world, often large, problems. In practice, the usage of heuristic based approaches trade-off completeness for time efficiency. An example of such heuristic approaches is STACCATO, which was proposed in the context of reasoning-based fault localization. In this paper, we propose an efficient distributed algorithm, dubbed MHS2, that renders the sequential search algorithm STACCATO suitable to distributed, Map-Reduce environments. The results show that MHS2 scales to larger systems (when compared to STACCATO), while entailing either marginal or small run time overhead