Educational Data Mining (Konsep Dan Penerapan)

Electronic Learning (E-Elarning), especially Web-based learning or intelligent tutorial system has yielded information about the interaction between students and the system. This information is stored in a database in the form of a web log. Within a certain period it will be a large amount of information. From the collection of large amounts of data that can be explored using data mining methods to generate new patterns that can be useful for improving the effectiveness of computer-based learning process. This paper will discuss how data mining can be utilized to improve the effectiveness of computer-based learning process. In its application the data will be processed in three stages: the collection, transformation, and analysis. Then the techniques used in the analysis algorithm is Association rules, classification, and clustering

Fairness-aware Machine Learning in Educational Data Mining

Fairness is an essential requirement of every educational system, which is reflected in a variety of educational activities. With the extensive use of Artificial Intelligence (AI) and Machine Learning (ML) techniques in education, researchers and educators can analyze educational (big) data and propose new (technical) methods in order to support teachers, students, or administrators of (online) learning systems in the organization of teaching and learning. Educational data mining (EDM) is the result of the application and development of data mining (DM), and ML techniques to deal with educational problems, such as student performance prediction and student grouping. However, ML-based decisions in education can be based on protected attributes, such as race or gender, leading to discrimination of individual students or subgroups of students. Therefore, ensuring fairness in ML models also contributes to equity in educational systems. On the other hand, bias can also appear in the data obtained from learning environments. Hence, bias-aware exploratory educational data analysis is important to support unbiased decision-making in EDM. In this thesis, we address the aforementioned issues and propose methods that mitigate discriminatory outcomes of ML algorithms in EDM tasks. Specifically, we make the following contributions: We perform bias-aware exploratory analysis of educational datasets using Bayesian networks to identify the relationships among attributes in order to understand bias in the datasets. We focus the exploratory data analysis on features having a direct or indirect relationship with the protected attributes w.r.t. prediction outcomes. We perform a comprehensive evaluation of the sufficiency of various group fairness measures in predictive models for student performance prediction problems. A variety of experiments on various educational datasets with different fairness measures are performed to provide users with a broad view of unfairness from diverse aspects. We deal with the student grouping problem in collaborative learning. We introduce the fair-capacitated clustering problem that takes into account cluster fairness and cluster cardinalities. We propose two approaches, namely hierarchical clustering and partitioning-based clustering, to obtain fair-capacitated clustering. We introduce the multi-fair capacitated (MFC) students-topics grouping problem that satisfies students' preferences while ensuring balanced group cardinalities and maximizing the diversity of members regarding the protected attribute. We propose three approaches: a greedy heuristic approach, a knapsack-based approach using vanilla maximal 0-1 knapsack formulation, and an MFC knapsack approach based on group fairness knapsack formulation. In short, the findings described in this thesis demonstrate the importance of fairness-aware ML in educational settings. We show that bias-aware data analysis, fairness measures, and fairness-aware ML models are essential aspects to ensure fairness in EDM and the educational environment.Ministry of Science and Culture of Lower Saxony/LernMINT/51410078/E

Student Performance Prediction Using Educational Data Mining Techniques

Educational sector produces data in large amount that is too voluminous and complex to understand. There is a need to efficiently filter and prioritize the data so as to deliver the relevant information to get rid of information overloading. Data mining searches through the large amount of dynamically generated data to present users with the useful and understandable patterns and trends. It has the power to use the raw data effectively which has been produced by universities, to draw the hidden patterns and the relationships among the attributes that are used in predicting the student performance, their behaviour effectively. In this paper the data mining techniques have been briefly described. The literature review of educational data mining is also done. This paper, implements data mining techniques such as Naive bayes and Support vector machine to predict the student performance

Ensemble Learning Methods for Educational Data Mining Applications

Student success efficacy studies are aimed at assessing instructional practices and learning environments by evaluating the success of and characterizing student subgroups that may benefit from such modalities. We develop an ensemble learning approach to perform these analytics tasks with specific focus on estimating individualized treatment effects (ITE). ITE are a measure from the personalized medicine literature that can, for each student, quantify the impact of the intervention strategy on student performance, even though the given student either did or did not experience this intervention (i.e., is either in the treatment group or in the control group). We illustrate our learning analytics methods in the study of a supplemental instruction component for a large enrollment introductory statistics course recognized as a curriculum bottleneck at San Diego State University. As part of this application, we show how the ensemble estimate of the ITE may be used to assess the pedagogical reform (supplemental instruction), advise students into supplemental instruction at the beginning of the course, and quantify the impact of the supplemental instruction component on at-risk subgroups. Higher Education researchers and Institutional Research practitioners struggle with the analysis of observational study data and estimation of treatment effects. Propensity score matching has widely been accepted to counteract inherent selection bias in these studies. We present an ensemble learner for propensity score estimation, and consider the use of inverse probability of treatment weighting (IPTW), variance stabilization weighting, and weight truncation to improve treatment effect estimation over propensity score matching. We run a simulation study to validate the treatment effect and propensity score estimation performance of the ensemble learner compared to logistic regression and random forest within the matching and weighting techniques. The results show that the use of the ensemble learner and variance stabilization with truncation result in the lowest mean squared error for treatment effect estimation. We contribute a new package in the statistical software environment R, matchED, that will provide educational researchers with a tool to help analyze student success study data and present actionable results. A tutorial guides the user through the use of each function and it\u27s parameters. A student success intervention is evaluated using the matchED package, and we are able to show that the intervention does help reduce an inherent equity gap between students in the intervention and their peers