Open Access

ARTICLE

Short Text Mining for Classifying Educational Objectives and Outcomes

Yousef Asiri^*

College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia

* Corresponding Author: Yousef Asiri. Email:

Computer Systems Science and Engineering 2022, 41(1), 35-50. https://doi.org/10.32604/csse.2022.020100

Received 09 May 2021; Accepted 11 June 2021; Issue published 08 October 2021

Abstract

Most of the international accreditation bodies in engineering education (e.g., ABET) and outcome-based educational systems have based their assessments on learning outcomes and program educational objectives. However, mapping program educational objectives (PEOs) to student outcomes (SOs) is a challenging and time-consuming task, especially for a new program which is applying for ABET-EAC (American Board for Engineering and Technology the American Board for Engineering and Technology—Engineering Accreditation Commission) accreditation. In addition, ABET needs to automatically ensure that the mapping (classification) is reasonable and correct. The classification also plays a vital role in the assessment of students’ learning. Since the PEOs are expressed as short text, they do not contain enough semantic meaning and information, and consequently they suffer from high sparseness, multidimensionality and the curse of dimensionality. In this work, a novel associative short text classification technique is proposed to map PEOs to SOs. The datasets are extracted from 152 self-study reports (SSRs) that were produced in operational settings in an engineering program accredited by ABET-EAC. The datasets are processed and transformed into a representational form appropriate for association rule mining. The extracted rules are utilized as delegate classifiers to map PEOs to SOs. The proposed associative classification of the mapping of PEOs to SOs has shown promising results, which can simplify the classification of short text and avoid many problems caused by enriching short text based on external resources that are not related or relevant to the dataset.

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Citations