Anwar Ali Yahya


In outcome-based education, program education objects (PEOs) are essential components around which all program’s activities are centered. They represent graduates professional and career accomplishments within few years of graduation. In this paper, the relationship between the academic majors and PEOs of engineering programs is questioned and a data analytics-based approach to answer this question is proposed. More specifically, this paper applies three well-known data correlation measures, namely Pointwise Mutual Information, Correlation Coefficient, and Odds Ratio, to a PEOs dataset extracted from the self-study reports of a set of Engineering programs. The PEOs dataset has been linguistically pre-processed through cleaning, annotation using a set of PEOs labels, and projection to break down each multi-PEOs label data instance into several single PEOs data instances. After that, the three measures are applied to measure the relationship between Programs' Majors (PMs) and PEOs. The obtained results are then ranked based on PMs-PEOs correlation strength and the agreement analysis among the three measures show a remarkable consistency among them in their evaluation of the relationship between PMs and PEOs. Finally, the overall ranking of PEOs within each PM, computed as a majority vote of the ranking of the three measures, show that each PM has a unique pattern of ranked PEOs. This suggests that the nature of PM plays a key role in determining the PM-PEOs relationship pattern. The obtained PMs-PEOs quantitative correlations are very beneficial to the academicians particularly when designing new programs or reviewing existing ones.


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educational data analytics, learning analytics, program educational objectives, outcome-based education

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A. Glatthorn, "Outcome based education: Reform and the curriculum process," Journal of Curriculum and Supervision, vol. 4, pp. 354-363, 1993.

T. Guskey, "Defining the differences between the outcome based education and mastery learning," The School. Administrator, vol. 52, pp. 34-37, 1994.

W. Spady, Outcome-based education: Critical issues and answers, Arlington, VA: American Association of School Administrators, 1994.

ABET, "Criteria for accrediting engineering programs: effective for evaluations during the 2018-2019 cycle," 2019. [Online]. Available: https://www.abet.org/accreditation/accreditation-criteria/criteria-for-accrediting-engineering-programs-2018-2019/. [Accessed 11 February 2022].

N. J. Mourtos, "A Systematic Approach for Defining and Assessing Program Educational Objectives and Outcomes," in The World Congress on Computer Science, Engineering and Technology Education, 2006.

M. Carter, R. Brent and S. Rajala, "EC 2000 Criterion 2: A procedure for creating, assessing, and documenting program educational objectives," in 2001 ASEE Annual Conference, 2001.

M. I. Khan, S. M. Mourad and W. M. Zahid, "Developing and qualifying Civil Engineering Programs for ABET accreditation," Journal of King Saud University – Engineering Sciences, pp. 1-11, 2014.

N. Abbadeni, A. Ghoneim and A. Alghamdi, "Program Educational Objectives Definition and Assessment for Quality and Accreditation," International Journal of Engineering Pedagogy, vol. 3, 2013.

A. Nguyen, L. Gardner and D. Sheridan, "Data Analytics in Higher Education: An Integrated View," Journal of Information Systems Education, vol. 31, no. 1, pp. 61-71, 2020.

R. S. J. Baker, "Data mining for education," in International encyclopedia of education, 2011.

G. Siemens and P. Long, "Penetrating the Fog: Analytics in Learning and Education," EDUCAUSE Review, vol. 46, no. 5, pp. 30-40, 2011.

W. Greller and H. Drachsler, "Translating Learning into Numbers: A Generic Framework for Learning Analytics," Educational Technology & Society, vol. 15, no. 3, pp. 42-57, 2012.

A. Nguyen, L. Gardner and D. Sheridan, "A Multi-Layered Taxonomy of Learning Analytics Applications," in Proceedings of the Pacific Asia Conference on Information Systems, 2017.

R. Baker and P. S. Inventado, "Educational Data Mining and Learning Analytics," in Learning Analytics: From Research to Practice, New York, NY, Springer, 2014, pp. 61-75.

A. Nguyen, L. Gardner and D. Sheridan, "Building an Ontology of Learning Analytics," in Proceedings of the Pacific Asia Conference on Information Systems, 2018.

G. Siemens and R. Baker, "Learning Analytics and Educational Data Mining: Towards Communication and Collaboration," in Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 2012.

G. Siemens, "Learning Analytics: The Emergence of a Discipline," American Behavioral Scientist, vol. 57, no. 10, pp. 1380-1400, 2013.

E. Dahlstrom, D. C. Brooks and J. Bichsel, "The Current Ecosystem of Learning Management Systems in Higher Education: Student, Faculty, and IT Perspectives," EDUCAUSE Review, 2014.

G. Mendez, X. Ochoa, K. Chiluiza, Chiluiza and B. d. Wever, "Curriculum design analysis: a data-driven perspective," Learning Analytics, vol. 1, no. 3, pp. 84-119, 2014.

S. Zhong-mei, Q. Qiong-fei and F. Lu-qi, "Educational data mining and analyzing of student learning outcome from the perspective of learning experience," in 7th International Conference on Educational Data Mining, 2014.

R. Wes Crues, "Untangling The Program Name Versus The Curriculum: An Investigation of Titles and Curriculum Content," in 10th International Conference on Educational Data Mining, 2017.

K. Goslin and M. Hofmann, "Identifying and visualizing the similarities between course content at a learning object, module and program level," in 6th International Conference on educational data mining, 2013.

Y. H. Jiang and L. Golab, "On Competition for Undergraduate Co-op Placements: A Graph Mining Approach," in 9th International Conference of Educational Data Mining, 2016.

T. Winters, Educational Data Mining: Collection and Analysis of Score Matrices for Outcomes-Based Assessment, University of California Riverside, 2006.

S. Chopra, H. Gautreau, A. Khan, M. Mirsafian and L. Golab, "Gender Differences in Undergraduate Engineering Applicants: A Text Mining Approach," in Educational Data Mining Conference, 2018.

M. Deziel, D. Olawo, L. Truchon and L. Golab, "Analyzing the mental health of engineering students using classification and regression," in 6th International Conference of Educational Data Mining, 2013.

Y. Bouslimani, G. Durand and N. Belacel, "Educational Data Mining approach for engineering graduate attributes analysis," in The Canadian Engineering Education Association (CEEA) Conference, Dalhousie University, 2016.

M. Khair, C. El Moucary and W. Zakhem, "Creating an Educational Roadmap for Engineering Students via an Optimal and Iterative Yearly Regression Tree using Data Mining," in the 4th International Conference on Knowledge Engineering and Ontology Development, Barcelona, Spain, 2012.

N. Buniyamin, U. Bin Mat and P. M. Arshad, "Educational Data Mining for Prediction and Classification of Engineering Students Achievement," in IEEE 7th International Conference on Engineering Education (ICEED), 2015.

A. Sharabiani, F. Karim, A. Sharabiani and A. Atanaso, "An enhanced Bayesian network model for prediction of students' academic performance in engineering programs," in IEEE Global Engineering Education Conference (EDUCON), 2014.

A. Osman, A. A. Yahya and B. Kamal, "A benchmark collection for mapping program educational objectives to ABET student outcomes: Accreditation," in 5th International Symposium on Data Mining Applications, 2018.

K. W. Church and P. Hanks, "Word association norms, mutual information, and lexicography," Computational Linguistics, vol. 16, no. 1, pp. 22-29, 1990.

D. Mladeni and G. Marko, "Feture selection for unbalanced class distribution and naive bayes," in The Sixteenth International Conference on Machine Learning, 1999.

H. Ng, W. Goh and K. Low, "Feature selection, perceptron learning, and a usability case study for text categorization," in ACM SIGIR Conference on Research and Developement in Information Retrieval, 1997.

R. Pel ́anek, "Measuring similarity of educational items: An overview," IEEE Transactions on Learning Technologies, 2019.

DOI: http://dx.doi.org/10.46827/ejes.v9i5.4285


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