A data‐mining‐based approach to informed decision‐making in engineering education

Yahya, Anwar Ali; Osman, Addin

doi:10.1002/cae.22158

Cited by 13 publications

(12 citation statements)

References 22 publications

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“…As students are the beneficiaries of higher education institutions, analyzing these transitional data based on the learning outcomes can provide a useful notion of the quality of education offered by the university. Also, an analysis of this trait can be useful in administrative planning and decision‐making of educational institutions' accreditation process that contributes to continuous quality improvement [17,28]. The academic data analysis related to the students will allow the institution to validate the achievement of specific learning outcomes [8].…”

Section: Results and Analysismentioning

confidence: 99%

“…Fortunately, for this case study, the selected dataset is of good quality with no missing values. However, if the datasets have missing values and outliers, then they must be appropriately identified and processed [24,28]. Clustering is done using k-means and k-medoids techniques by taking the Euclidean distance using the RapidMiner tool.…”

Section: Data Preparation and Methodologymentioning

confidence: 99%

“…Data mining is the process of discovering information from huge datasets to derive knowledge from the data provided. It comprises of dealing with various types of patterns [28]. Data mining techniques are generally categorized into supervised learning techniques and unsupervised learning techniques.…”

Section: Cluster Analysis/ Clustering Techniquesmentioning

confidence: 99%

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Investigating learning outcomes in engineering education with data mining

Mahboob

Ali

Laila

2020

Comp Applic In Engineering

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Higher education institutions are catching up on their high competition and challenges are in their analysis productivity. The major challenge is to monitor and analyze student progress through learning outcomes in the curriculum. One of the approaches is the outcome-based education (OBE) model to deal with learning outcomes. OBE is an integral part of higher education institutions. The OBE system is a key step for accreditation in engineering education. OBE focuses on a student-centered approach. The OBE is not restricted to welldefined teaching strategies or direct evaluations but also encompasses indirect evaluations to help students achieve the intended outcomes. In this investigation, engineering students' data have been analyzed forming three distinct clusters to group students according to best, average, and worst achievement of learning outcomes in two different computer engineering courses generally taught in the early semesters in higher education institutions. A data mining clustering approach is used to segment students using k-means and k-medoids techniques. Clustering can be regarded as a data modeling technique that provides summary data that interact with multiple disciplines and plays an important role in a wide range of computer applications. The investigation comprises of two parts for analysis: one part of the analysis is the mid-term and final exam scores, the quiz and assignment results, the laboratory results, and the evaluation, together with the learning outcomes achieved, and the other part is the comparative analysis of learning outcomes achieved in both engineering courses clustering with the best, average, and worst attainments, respectively. In this investigation, the results obtained from clustering data points show that the same group of clusters with the best, average, and worst learning outcomes achievements formed using both k-means and k-medoid clustering for one course. On the other hand, a diverse group of clusters with the best, average, and worst learning outcomes achievements formed using both k-means and k-medoids clustering for another course.

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Section: Results and Analysismentioning

confidence: 99%

Section: Data Preparation and Methodologymentioning

confidence: 99%

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Investigating learning outcomes in engineering education with data mining

Mahboob

Ali

Laila

2020

Comp Applic In Engineering

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“…In this work the PEOs-SOs mapping data are collected from the SSRs of 215 engineering programs accredited by ABET-EAC between 2000 and 2019. It should be noted that collected raw data have been used by the authors in several previous pieces of research [29,30], and more details can be found in [31]. Figure 3 is a snippet of the PEOs-SOs data.…”

Section: Raw Data Collectionmentioning

confidence: 99%

Toward a Better Understanding of Academic Programs Educational Objectives: A Data Analytics-Based Approach

et al. 2021

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In outcome-based academic programs, Program Education Objects (PEOs) are the key pillars on which program components are built. They are articulated linguistically as broad statements of graduates’ professional and career accomplishments within a few years of graduation. Moreover, PEOs are mapped into a set of skills and attributes known as Program Learning Outcomes (PLOs). It goes without saying that a profound understanding of the PEOs is a key factor in the success of an academic program. For this sake, this paper proposes a data analytics-based approach to examine the correlations among PEOs. More specifically, it applies a data similarity-based approach to analyze the correlations among the PEOs of engineering programs. To this end, a dataset of PEOs–PLOs mapping of a set of engineering programs has been extracted from their self-study reports. The collected dataset has undergone preprocessing steps to transform it into a suitable representation. This involves data cleaning, data annotation using a developed set of PEOs labels, and removal of data instances with multiple PEO labels. Each PEO is then represented as a vector space model whose dimensions are the PLOs, and their values are the relative frequencies of PLOs computed from all data instances of that PEO. After that, three data similarity measures, namely Euclidean distance, cosine measure, and Manhattan distance, are applied to measure the similarity between PEOs vector space models. The resultant similarity matrices are then analyzed at the level of a specific measure, an agreement between measures, and average similarity across all measures. The analysis results contribute to a better understanding of the PEOs correlations and provide very useful actionable insights for empowering decision making toward systemization and optimization of academic programs processes.

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“…Characterizations of the flipped-based teaching in classroom are discussed in many ways. Few of them focus on the usage of digital technology [7,8], few of them target social media [9], and few discuss the significance of applying a certain pedagogical way, like mastery-based learning [10] or cooperative learning method [11].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the College English Flipped Classroom Teaching Model Based on Data Mining Algorithms

Xiang

2021

Mobile Information Systems

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Aiming at the problem of the inability to classify data due to the excessive amount of teaching resources, which leads to the college English flipped classroom teaching model’s low resource sharing rate and the poor accuracy of score statistical analysis, a university-based data mining algorithm is designed. Research on the evaluation of english flipped classroom teaching model is conducted, the strategy of applying the flipped classroom in college English teaching is analyzed, the characteristics and advantages of this model are explored, the data mining algorithm to practical teaching is applied, and the decision tree C4.5 classification technology is used to achieve accurate classification of massive student test scores. The classification technology selects classification attributes based on the information gain rate. It uses the postpruning method to process data to improve the accuracy of data classification. Finally, the statistical analysis results of the business logic layer are transmitted to the user through the browser application layer using the WEB server. The experimental results show that using this article’s evaluation method, the college English flipped classroom teaching model can achieve a high resource sharing rate, high accuracy of performance statistics analysis, and a good teaching effect.

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A data‐mining‐based approach to informed decision‐making in engineering education

Cited by 13 publications

References 22 publications

Investigating learning outcomes in engineering education with data mining

Investigating learning outcomes in engineering education with data mining

Toward a Better Understanding of Academic Programs Educational Objectives: A Data Analytics-Based Approach

Evaluation of the College English Flipped Classroom Teaching Model Based on Data Mining Algorithms

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