Translating network position into performance: Importance of centrality in different network configurations

As the field of learning analytics continues to mature, there is a corresponding evolution and sophistication of the associated analytical methods and techniques. In this regard social network analysis (SNA) has emerged as one of the cornerstones of learning analytics methodologies. However, despite the noted importance of social networks for facilitating the learning process, it remains unclear how and to what extent such network measures are associated with specific learning outcomes. Motivated by Simmel’s theory of social interactions and building on the argument that social centrality does not always imply benefits, this study aimed to further contribute to the understanding of the association between students ’ social centrality and their academic performance. The study reveals that learning analytics research drawing on SNA should incorporate both – descriptive and statistical methods to provide a more comprehensive and holistic understanding of a students ’ network position. In so doing researchers can undertake more nuanced and contextually salient inferences about learning in network settings. Specifically, we show how differences in the factors framing students ’ interactions within two instances of a MOOC affect the association between the three social network centrality measures (i.e., degree, closeness, and betweenness) and the final course outcome

The Implementation of Clique Strategy in Regrouping Program to Increase Farmer’s Interest and Loyalty in Sugarcane Farming

The area size of sugarcane planting nationally shows a decrease. This is also marked by a decrease in the interest of farmers planting sugarcane and low loyalty to the sugar factory. In 2014, the government established a sugarcane management regrouping program. The regrouping program provides benefits for the efficiency of sugarcane planting, more effective in cutting and transport processes, forging farmer group and sugar factory, and increasing farmers' loyalty to the sugar factory.  The weakness of the regrouping program is that it emphasizes technical aspects only. The criterion of the social aspect is essential because this always saves problems related to communication networks and establishing cooperation. Through the clique strategy, this can conduct a communication network analysis in regrouping. Regrouping programs can build social interaction and communication networks. The form and level of communication network structure in the regrouping program can place actors or farmers build communication networks and cooperate in sugarcane farming. Understanding the role of cooperation networks between farmers or groups determines the success of regrouping sugarcane.JEL Classification:  P32, O35, Q1

LearnFCA: A Fuzzy FCA and Probability Based Approach for Learning and Classification

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering. This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of it’s applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems. We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success. Adviser: Dr Jitender Deogu