In higher educational institutes, many students have to struggle hard to complete different courses since there is no dedicated support offered to students who need special attention in the registered courses. Machine learning techniques can be utilized for students' grades prediction in different courses. Such techniques would help students to improve their performance based on predicted grades and would enable instructors to identify such individuals who might need assistance in the courses. In this paper, we use Collaborative Filtering (CF), Matrix Factorization (MF), and Restricted Boltzmann Machines (RBM) techniques to systematically analyze a real-world data collected from Information Technology University (ITU), Lahore, Pakistan. We evaluate the academic performance of ITU students who got admission in the bachelor's degree program in ITU's Electrical Engineering department. The RBM technique is found to be better than the other techniques used in predicting the students' performance in the particular course.
Dropout early warning systems help higher education institutions to identify students at risk, and to identify interventions that may help to increase the student retention rate of the institutes. @cite_34 utilized the Wisconsin DEWS approach to predict the student dropout risk. They introduced flexible series of DEWS software modules that can adapt to new data, new algorithms, and new outcome variables to predict the dropout risk as well as impute key predictors.
@cite_34
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The state of Wisconsin has one of the highest four year graduation rates in the nation, but deep disparities among student subgroups remain. To address this the state has created the Wisconsin Dropout Early Warning System (DEWS), a predictive model of student dropout risk for students in grades six through nine. The Wisconsin DEWS is in use statewide and currently provides predictions on the likelihood of graduation for over 225,000 students. DEWS represents a novel statistical learning based approach to the challenge of assessing the risk of non-graduation for students and provides highly accurate predictions for students in the middle grades without expanding beyond mandated administrative data collections. Similar dropout early warning systems are in place in many jurisdictions across the country. Prior research has shown that in many cases the indicators used by such systems do a poor job of balancing the trade off between correct classification of likely dropouts and false-alarm (, 2013). Building on this work, DEWS uses the receiver-operating characteristic (ROC) metric to identify the best possible set of statistical models for making predictions about individual students. This paper describes the DEWS approach and the software behind it, which leverages the open source statistical language R (R Core Team, 2013). As a result DEWS is a flexible series of software modules that can adapt to new data, new algorithms, and new outcome variables to not only predict dropout, but also impute key predictors as well. The design and implementation of each of these modules is described in detail as well as the open-source R package, EWStools, that serves as the core of DEWS (Knowles, 2014). We develop early warning systems to predict at-risk students while a course is in progress.Learning portfolios from a fully online course are evaluated by data mining techniques.The results show that CART supplemented by AdaBoost has the best classification performance.Time-dependent variables are essential to identify student online learning performance. An early warning system can help to identify at-risk students, or predict student learning performance by analyzing learning portfolios recorded in a learning management system (LMS). Although previous studies have shown the applicability of determining learner behaviors from an LMS, most investigated datasets are not assembled from online learning courses or from whole learning activities undertaken on courses that can be analyzed to evaluate students' academic achievement. Previous studies generally focus on the construction of predictors for learner performance evaluation after a course has ended, and neglect the practical value of an "early warning" system to predict at-risk students while a course is in progress. We collected the complete learning activities of an online undergraduate course and applied data-mining techniques to develop an early warning system. Our results showed that, time-dependent variables extracted from LMS are critical factors for online learning. After students have used an LMS for a period of time, our early warning system effectively characterizes their current learning performance. Data-mining techniques are useful in the construction of early warning systems; based on our experimental results, classification and regression tree (CART), supplemented by AdaBoost is the best classifier for the evaluation of learning performance investigated by this study. Retention of students at colleges and universities has been a concern among educators for many decades. The consequences of student attrition are significant for students, academic staffs and the universities. Thus, increasing student retention is a long term goal of any academic institution. The most vulnerable students are the freshman, who are at the highest risk of dropping out at the beginning of their study. Therefore, the early identification of students is a crucial task that needs to be effectively addressed. In this paper, we develop a survival analysis framework for early prediction of student dropout using Cox proportional hazards regression model (Cox). We also applied time-dependent Cox (TD-Cox), which captures time-varying factors and can leverage those information to provide more accurate prediction of student dropout. For this prediction task, our model utilizes different groups of variables such as demographic, family background, financial, high school information, college enrollment and semester-wise credits. The proposed framework has the ability to address the challenge of predicting dropout students as well as the semester that the dropout will occur. This study enables us to perform proactive interventions in a prioritized manner where limited academic resources are available. This is critical in the student retention problem because not only correctly classifying whether a student is going to dropout is important but also when this is going to happen is crucial for a focused intervention. We evaluate our method on real student data collected at Wayne State University. Results show that the proposed Cox-based framework can predict the student dropouts and semester of dropout with high accuracy and precision compared to the other state-of-the-art methods. The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program as well as identifying success-factors specific to the EE program. Our experimental results show that rather simple and intuitive classifiers (decision trees) give a useful result with accuracies between 75 and 80 . Besides, we demonstrate the usefulness of cost-sensitive learning and thorough analysis of misclassifications, and show a few ways of further prediction improvement without having to collect additional data about the students.