Abstract
This research aims to present a collection of dataset, which represents the mapping of program education objectives to the ABET student outcomes. The dataset has been collected by the authors from 32 self-study reports from Engineering programs accredited by ABET, which are available online. The paper presents the constraints under which, the dataset was produced, because its understanding plays a vital role in the usage of this collection in future researches. To illustrate the properties and usefulness of the collection, the dataset has been cleansed, preprocessed, some features have been selected, then it has been benchmarked using nine of the widely used supervised multiclass classification techniques (Binary Relevance, Label Powerset, Classifier Chains, Pruned Sets, Random k-label sets, Ensemble of Classifier Chains, Ensemble of Pruned Sets, Multi-Label k Nearest Neighbors and Back-Propagation Multi-Label Learning). The techniques have been compared to each other using five well-known measurements (Accuracy, Hamming Loss, Micro-F, Macro-F, and Macro-F). The Ensemble of Classifier Chains and Ensemble of Pruned Sets have achieved encouraging performance compared to the other experimented multi-label classification methods. The Classifier Chains method has shown the worst performance. In general, promising results have been achieved. New research directions and baseline experimental results for future studies in educational data mining in general and in accreditation in specific have been provided.
Keywords
- Benchmark collection
- Program educational objectives
- Student outcomes
- ABET
- Accreditation
- Machine learning
- Supervised multiclass classification
- Text mining
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Fabrizio, S.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)
Shweta, C.D., Maya, I., Parag, K.: Empirical studies on machine learning based text classification algorithms. Adv. Comput. Int. J. (ACIJ) 2(6), 161–169 (2011)
Fabricio, A.B., Daniel, C., Guimarães P.: Combined unsupervised and semi-supervised learning for data classification. In: IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), Salerno, Italy, pp. 13–16 (2016)
Lunke, F., Yong, X., Xiaozhao, F., Jian, Y.: Low rank representation with adaptive distance penalty for semi-supervised subspace classification. Pattern Recogn. 67, 252–262 (2017). http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7605057
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Secaucus (2006)
Murphy, K.P.: Machine Learning: A Probabilistic Perspective, 1st edn. The MIT Press, Cambridge (2012)
Duda, R.O., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley-Interscience, New York (2000)
David, D.L., Robert, E.S., James, P.C., Ron, P.: Training algorithms for linear text classifiers. In: Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1996), pp. 298–306. ACM, New York (1996)
David, D.L.: Reuters-21578 text Categorization test collection. Distribution 1.0. Readme file (version 1.2). Manuscript (1997)
Yiming, Y.: An evaluation of statistical approaches to text categorization. Inf. Retrieval 1(1–2), 67–88 (1999)
David, D.L., Yiming, Y., Tony, G.R., Fan, L.: RCV1: a new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361–397 (2004)
Pratiksha, Y., Gawande, S.H.: A comparative study on different types of approaches to text categorization. Int. J. Mach. Learn. Comput. 2(4), 423–426 (2012)
ABET, ABET Strategic Plan, Accreditation Board for Engineering and Technology, Inc., ABET, 1 November 1997
Engineering Accreditation Commission (ABET), Criteria for Accrediting Engineering Programs Effective for Review During the 2015–2016 Accreditation Cycle, 415 N. Charles Street Baltimore, MD 21201, United States of Ameriaca, ABET (2014)
ABET, Criteria for Accrediting Engineering Programs Effective for Reviews During the 2016–2017 Accrediting Cycle
de Baker, R.S.J.: Data mining for education. In: McGaw, B., Peterson, P., Baker, E. (eds.) International Encyclopedia of Education, 3rd edn. Elsevier, Oxford (2010)
Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33(1), 135–146 (2007)
de Baker, R.S.J., Yacef, K.: The state of educational data mining in 2009: a review and future vision. J. Educ. Data Min. 1(1), 1–15 (2009)
Peña-Ayala, A., Domínguez, R., Medel, J.: Educational data mining: a sample of review and study case. World J. Educ. Technol. 2, 118–139 (2009)
Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(6), 601–618 (2010)
Ihantola, P., Vihavainen, A., Ahadi, A., Butler, M., Börstler, J., Edwards, S.H., Isohanni, E., Korhonen, A., Petersen, A., Rivers, K., Rubio, M.Á., Sheard, J., Skupas, B., Spacco, J., Szabo, C., Toll, D.: Educational data mining and learning analytics in programming: Literature review and case studies. In: Proceedings of the 2015 ITiCSE on Working Group Reports, Annual Conference on Innovation and Technology in Computer Science Education, pp. 41–63. ACM (2015). https://tutcris.tut.fi/portal/en/publications/educational-data-mining-and-learning-analytics-in-programming-literature-review-and-case-studies(6cd8ff1c-133a-4cf9-8a6e-ef61ba37ae7a).html
Fatima, D., Fatima, S., Prasad, A.V.K.: A survey on research work in educational data mining. J. Comput. Eng. 17(2), 43–49 (2015)
Papamitsiou, Z., Economides, A.: Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. Educ. Technol. Soc. 17(4), 49–64 (2014)
Isha, S., Dinesh, K., Mudit, K.: A review of applications of data mining techniques for prediction of students’ performance in higher education. J. Stat. Manage. Syst. 20(4), 713–722 (2017). https://www.tandfonline.com/doi/abs/10.1080/09720510.2017.1395191
Raheela, A., Agathe, M., Syed Abbas, A., Najmi, G.H.: Analyzing undergraduate students’ performance using educational data mining. Comput. Educ. 113, 177–194 (2017)
Anwar, A.Y., Addin, O.: Automatic classification of questions into Bloom’s cognitive levels using support vector machines. In: The International Arab Conference on Information Technology. Naif Arab University for Security Science (NAUSS), Riyadh, Saudi Arabia (2013)
Anwar, A.Y., Addin, O., Mohammad, S.E.: Rocchio algorithm-based particle initialization mechanism for effective PSO classification of high dimensional data. Swarm Evol. Comput. 34, 18–32 (2017). https://www.sciencedirect.com/journal/swarm-and-evolutionary-computation
Addin, O., Anwar, A., Y.: Classifications of exam questions using linguistically-motivated features: a case study based on Bloom’s taxonomy. In: The Third International Arab Conference on Quality Assurance in Higher Education (IACQA 2016), pp. 889–896. Khartoum Sudan (2016)
Hamalainen, W., Vinni, M.: Comparison of machine learning methods for intelligent tutoring systems. In: ITS 2006 Proceedings of the 8th International Conference on Intelligent Tutoring Systems, Jhongli, Taiwan, pp. 525–534 (2006)
Mohamad, S.K., Tasir, Z.: Educational data mining: a review. In: The 9th International Conference on Cognitive Science, pp. 320–324. Procedia - Social and Behavioral Sciences, Kuching, Sarawak, Malaysia (2013)
Ronald, D.: The Importance of Having Data-sets. In: Proceedings of the IATUL Conferences, Paper 16 (2006)
Anwar, A.Y., Zakaria, T., Addin, O.: Bloom’s Taxonomy–based classification for item bank questions using support vector machines. In: Modern Advances in Intelligent Systems and Tools, vol. 431, pp. 135–140 (2012). https://link.springer.com/book/10.1007/978-3-642-30732-4
Anwar, A.Y., Addin, O.: Automatic classification of questions into Bloom’s cognitive levels using support vector machines. In: The International Arab Conference on Information Technology, pp. 335–342. Naif Arab University for Security Science (NAUSS), Riyadh, Saudi Arabia (2011). https://scholar.google.com/scholar?oi=bibs&cluster=11863385617269352176&btnI=1&hl=en
Anwar, A.Y., Addin, O., Ahmed A.A.: Educational data mining: a case study of teacher’s classroom questions. In: 13th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 34–41. UPM, Selangor (2013). http://ieeexplore.ieee.org/abstract/document/6920714/
Koller, D., Sahami, M.: Hierarchically classifying documents using very few words. In: International Conference on Machine Learning (ICML 1997), Nashville, Tennessee, pp. 170–178 (1997)
Weigend, A.S., Wiener, E.D., Pedersen, J.O.: Exploiting hierarchy in text categorization. Inf. Retrieval 1(3), 193–216 (1999)
Steven, B., Ewan, K., Edward, L.: Natural Language Processing with Python, 1st edn. O’Reilly Media, USA (2009)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer, Heidelberg (2010)
Jesse, R., Peter, R., Bernhard, P., Geoff, H.: MEKA: a multi-label/multi-target extension to Weka. J. Mach. Learn. Res. 17(21), 1–5 (2016)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, Elsevier, Amsterdam (2005)
Read, J., Pfahringer, B., Holmes, G.: Multi-label classification using ensembles of pruned sets. In: 8th IEEE International Conference on Data Mining, Pisa, Italy, pp. 995–1000. IEEE Computer Society (2008)
Sajnani, H., Javanmardi, S., McDonald, D.W., Lopes, C.V.: Multi-label classification of short text: a study on wikipedia barnstars. In: Analyzing Microtext: the Proceeding of the 2011 AAAI Workshop (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Osman, A., Yahya, A.A., Kamal, M.B. (2018). A Benchmark Collection for Mapping Program Educational Objectives to ABET Student Outcomes: Accreditation. In: Alenezi, M., Qureshi, B. (eds) 5th International Symposium on Data Mining Applications. Advances in Intelligent Systems and Computing, vol 753. Springer, Cham. https://doi.org/10.1007/978-3-319-78753-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-78753-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-78752-7
Online ISBN: 978-3-319-78753-4
eBook Packages: EngineeringEngineering (R0)