An overview of using academic analytics to predict and improve students' achievement: A proposed proactive intelligent intervention

Mat, Usamah Bin; Buniyamin, Norlida; Arsad, Pauziah Muhd; Kassim, Rosni Abu

doi:10.1109/iceed.2013.6908316

Cited by 68 publications

(40 citation statements)

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“…And GPA is the main attribute to predict student performance, no doubt because GPA is a measure of student achievement that is internationally recognized and widely used and understood. Moreover, it is demonstrated through the coefficient correlation analysis, to be the most significant input variable [5]. Furthermore, GPA has been revealed as the strongest predictor of student performance [6] 27].…”

Section: Data Mining In Education Fieldmentioning

confidence: 99%

Finding Most Effective Sequences of Modules for Students in Higher Education to Improve their Performance using Knowledge Discovery in Databases

Bousbahi¹

2017

IJCA

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Universities offer a wide selection of courses to students studying in different fields; however, this has generated various unintended problem such as students struggling, dropping or failing. Universities also, own large amounts of historical data about students containing rich knowledge that can be used to facilitate a broad range of educational research and analytics study in order to enhance student performance. Knowledge discovery in databases (KDD) is the process of finding comprehensible patterns that can be interpreted as useful or interesting knowledge. One component in this process is Data Mining which is the application of machine learning algorithms for extracting patterns from data. In this context, data mining techniques are used to answer educational research questions that highlight students" success in higher education. Several challenges are facing higher education, one of which is finding the most effective sequence of modules for students for each semester. This finding may help students to improve their performance. In this research we investigate dependency between set of courses chosen by students at each level and their performance using a classification technique in order to build a performance prediction model, which is based on previous students" academic records.The finding of the study furnishes useful information to advisors in educational institution and valuable knowledge to feed analytics based systems and learning recommender systems to assist students selecting future modules to obtain optimum pathways courses.

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Section: Data Mining In Education Fieldmentioning

confidence: 99%

Finding Most Effective Sequences of Modules for Students in Higher Education to Improve their Performance using Knowledge Discovery in Databases

Bousbahi¹

2017

IJCA

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“…MT1 and MT2 are input parameters and graduation grade is output parameter as shown in Figure 2. [6]. Numeric data was gathered from Students Information Management System (SIMS) at UiTM.…”

Section: Cross Validationmentioning

confidence: 99%

“…Though engineering education [6,7]. EDM is a discipline that uses data from education setting such as universities and colleges to gain knowledge for better education planning [8].…”

Section: Introductionmentioning

confidence: 99%

Using Neuro-Fuzzy Technique to Classify and Predict Electrical Engineering Students’ Achievement Upon Graduation Based on Mathematics Competency

Mat

Buniyamin

2017

IJEECS

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“…The importance of data mining techniques in the field of education in order to enhance student understanding was highlighted in this research paper [17]. A list of analytical software tools used by universities was pro-vided.…”

Section: Software Tools For Learning Analyticsmentioning

confidence: 99%

A Study of Application of Data Mining and Analytics in Education Domain

Karkhanis¹,

Dumbre²

2015

IJCA

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Data Mining techniques and algorithms have been used on a large scale in almost all the sectors which range from computer science, manufacturing industry, and healthcare industry. A recently introduced concept of academic analytics uses the data mining algorithms on the educational data of students and gives certain insights about the expected performances of the students, expected retention rate of students and percentage of resources properly utilized. These results also help administrators in decision making and answering certain questions like whether the faculty v/s student's ratio is giving satisfactory results or there is a change needed in the teaching methodology. Educational Data Mining is also another upcoming field and is an allied field of Academic analytics, but it focuses on the data mining algorithm outputs being given back to the faculties in order to properly assess the student's performance. Educational data mining basically helps the tutors modify their teaching strategies if the results with the current teaching model are not satisfactory. This paper basically is a study of certain research experiments which aim to apply data mining algorithms to educational data and contribute to the field of Academic analytics and Educational Data Mining.

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An overview of using academic analytics to predict and improve students' achievement: A proposed proactive intelligent intervention

Cited by 68 publications

References 1 publication

Finding Most Effective Sequences of Modules for Students in Higher Education to Improve their Performance using Knowledge Discovery in Databases

Finding Most Effective Sequences of Modules for Students in Higher Education to Improve their Performance using Knowledge Discovery in Databases

Using Neuro-Fuzzy Technique to Classify and Predict Electrical Engineering Students’ Achievement Upon Graduation Based on Mathematics Competency

A Study of Application of Data Mining and Analytics in Education Domain

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