This paper focuses on anticipating the drop-out among MOOC learners and helping in the identification of the reasons behind this dropout. The main reasons are those related to course design and learners behavior, according to the requirements of the MOOC provider Open-Classrooms. Two critical business needs are identified in this context. First, the accurate detection of at-risk droppers, which allows sending automated motivational feedback to prevent learners drop-out. Second, the investigation of possible drop-out reasons, which allows making the necessary personalized interventions. To meet these needs, we present a supervised machine learning based drop-out prediction system that uses Predictive algorithms (Random Forest and Gradient Boosting) for automated intervention solutions, and Explicative algorithms (Logistic Regression, and Decision Tree) for personalized intervention solutions. The performed experimentations cover three main axes; (1) Implementing an enhanced reliable dropout-prediction system that detects at-risk droppers at different specified instants throughout the course. (2) Introducing and testing the effect of advanced features related to the trajectories of learners' engagement with the course (backward jumps, frequent jumps, inactivity time evolution). (3) Offering a preliminary insight on how to use readable classifiers to help determine possible reasons for drop-out. The findings of the mentioned experimental axes prove the viability of reaching the expected intervention strategies.
Several researchers and clinicians have questioned the advantages and disadvantages of inpatient and outpatient treatment for people suffering from pathological gambling. This study compares the characteristics of pathological gamblers seeking inpatient and outpatient treatment. A total of 233 pathological gamblers (inpatients = 134, outpatients = 99) participated in the study. Results show that inpatients have more severe gambling problems than those receiving outpatient services. Similar results were obtained on most other related variables such as anxiety, depression, alcohol consumption, and comorbidity. These results are discussed in terms of the costs and benefits of these two treatment modalities.
This paper presents new methods of 3D visualization of graphs that allow to highlight nodes structural centrality. These methods consist in projecting, along the vertical axis, 2D graph representations on three 3D surfaces: 1) a half-sphere; 2) a cone and 3) a torus portion. The transition to 3D allows to better handle the visualization of complex and large data that 2D techniques are generally unable to provide. The 3D radial layout techniques reduce nodes and edges overlap and improve, in some cases, the perception of nodes connectivity by exploiting differently or better the display space.
Abstract. This paper presents the KEOPS data mining methodology centered on domain knowledge integration. KEOPS is a CRISP-DM compliant methodology which integrates a knowledge base and an ontology. In this paper, we focus first on the pre-processing steps of business understanding and data understanding in order to build an ontology driven information system (ODIS). Then we show how the knowledge base is used for the post-processing step of model interpretation. We detail the role of the ontology and we define a part-way interestingness measure that integrates both objective and subjective criteria in order to eval model relevance according to expert knowledge. We present experiments conducted on real data and their results.
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