Data Mining Practical Machine Learning Tools and Techniques

Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of educational data mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a preprocessing algorithm has to be enforced first and only then some specific data mining methods can be applied to the problems. One such preprocessing algorithm in EDM is clustering. Many studies on EDM have focused on the application of various data mining algorithms to educational attributes. Therefore, this paper provides over three decades long (1983–2016) systematic literature review on clustering algorithm and its applicability and usability in the context of EDM. Future insights are outlined based on the literature reviewed, and avenues for further research are identified.

A Systematic Review on Educational Data Mining

Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively used for prediction purpose. While there is a plethora of success stories in the literature, these techniques are mainly accessible to “computer science”, or more precisely, “artificial intelligence” literate educators. Indeed, the effective and efficient application of data mining methods entail many decisions, ranging from how to define student’s success, through which student attributes to focus on, up to which machine learning method is more appropriate to the given problem. This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success. For this, the literature has been reviewed, and the state-of-the-art has been compiled into a systematic process, where possible decisions and parameters are comprehensively covered and explained along with arguments. This study will provide to educators an easier access to data mining techniques, enabling all the potential of their application to the field of education.

https://link.springer.com/content/pdf/10.1186%2Fs41239-020-0177-7.pdf

Predicting academic success in higher education: literature review and best practices

Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

https://dl.acm.org/doi/pdf/10.1145/3293881.3295783

Predicting academic performance: a systematic literature review

The persistent growth of data in education continues. More institutes now store terabytes and even petabytes of educational data. Data complexity in education is increasing as people store both structured data in relational format and unstructured data such as Word or PDF files, images, videos and geo-spatial data. Indeed learning developers, universities, and other educational sectors confirm that tremendous amount of data captured is in unstructured or semi-structured format. Educators, students, instructors, tutors, research developers and people who deal with educational data are also challenged by the velocity of different data types, organizations as well as institutes that process streaming data such as click streams from web sites, need to update data in real time to serve the right advert or present the right offers to their customers. This analytical study is oriented to the challenges and analysis with big educational data involved with uncovering or extracting knowledge from large data sets by using different educational data mining approaches and techniques.

An Approachable Analytical Study on Big Educational Data Mining

The Learning Management System (LMS) is a common occurrence in most educational institutions. This system is a software application helping the educator in administration, facilitation, and tracking of course content to the learner. Educators have always been interested in understanding student interaction with systems like LMS. Such a system generates a plethora of data in a various form such as student performance on the individual course, activities, student behaviors, etc. The most prominent solutions involve performing dimensionality reduction technique to improve classifier accuracy and reducing the fewer error rates. Therefore, this study utilizes feature selection as a dimensionality reduction technique. The multiclass data were handled using the Learning Vector Quantization (LVQ) algorithm to identify significant predictors and thereby reducing the biased result. The efficiency of feature selection technique is evaluated with five different classifiers such as Linear Discriminate Analysis (LDA), Classification and Regression Tree (CART), k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF). The performance of the classifier is evaluated using the kappa statistics and confusion matrix. Our extensive experimental results show that RF classifier produces optimum kappa statistic (85 %) with LVQ. Keywords—learning management system; classification; student performance; kappa statistic

Can We Predict Student Learning Performance from LMS Data? A Classification Approach

In the context of one-on-one instruction, reflective dialogues help students advance their learning and improve their problem solving ability. The effectiveness of one-on-one instruction with respect to learning through dialogue is highlighted by researchers and educators. However, little if any, is known about how reflective dialogues may lead to learning improvement and predict students’ problem solving ability. This information can be extracted from large educational datasets using data mining techniques. Consequently, this study aims at mining USNA physics data set applying a two-level clustering approach to find patterns in the data and identify how reflective dialogues predict students’ problem solving ability. The results indicated that reflective group performed better on the hourly exams. Control subjects took lower average count of steps during problem solving activity and the average of duration was longer in control group. Also, higher average of correct answers and average count of attempts was found in reflective condition. Yet, control group had a higher level of incorrect answers as compared to reflective group. During the intervention, reflective subjects had higher level of average count of attempts and lesser average count of deletion. Reflective group asked for less hints, had lesser count of problem and requested for calculator less than control subjects. The results of the analysis help educators plan more effective tutorial dialogues in Intelligent Tutoring Systems (ITSs).

Applying Clustering Approach to Analyze Reflective Dialogues and Students' Problem Solving Ability

In the context of one-on-one instruction, reflective
dialogues help students advance their learning and improve their problem solving ability. The effectiveness of one-on-one instruction with respect to learning through dialogue is highlighted by researchers and educators. However, little if any, is known about how reflective dialogues may lead to learning improvement and predict students’ problem solving ability. This information can be extracted from large educational datasets using data mining techniques. Consequently, this study aims at mining USNA physics
dataset applying a two-level clustering approach to find patterns in the data and identify how reflective dialogues predict students’ problem solving ability. The results indicated that reflective group performed better on the hourly exams. Control subjects took lower average count of steps during problem solving activity and the average of duration was longer in control group. Also, higher average of correct answers and average count of attempts was found
in reflective condition. Yet, control group had a higher level of incorrect answers as compared to reflective group. During the intervention, reflective subjects had higher level of average count of attempts and lesser average count of deletion. Reflective group asked for less hints, had lesser count of problem and requested for calculator less than control subjects. The results of the analysis help
educators plan more effective tutorial dialogues in intelligent tutoring systems (ITSs).

Ashish Dutt

Papers

A Systematic Review on Educational Data Mining

An Approachable Analytical Study on Big Educational Data Mining

Can We Predict Student Learning Performance from LMS Data? A Classification Approach

Applying Clustering Approach to Analyze Reflective Dialogues and Students' Problem Solving Ability

Reflective dialogues and students’ problem solving ability analysis using clustering