Outcome Based Learning: A case study

Subbaraman, Shaila; Jagtap, Rupali; Shinde, Sunita S.

doi:10.1109/mite.2013.6756371

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…Having set the PEOs and SOs of an OBE academic program and established the mapping between them, the curriculum, teaching, and learning strategies, and assessment strategies are then designed in such a way that students ultimately gain knowledge and develop skills stated in the SOs. In this manner, the curriculum is viewed as a set of courses aim to attain certain Course Outcomes (COs) that map to SOs which themselves map to PEOs which in turn map to the mission of the institution in a hierarchical structure shown in Figure 1 [35] In practice, the PEOs are developed by program's management and constituents to connect the program with its context, whereas SOs are developed by program's management and faculty with the guidance of educational authorities or accreditation bodies such as ABET. Besides developing PEOs and SOs, the mapping between them should be carefully and informedly established.…”

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confidence: 99%

“…Despite the key importance of knowledgeable development of PEOs, SOs, and the mapping between them, there is a lingering confusion, among practitioners, related to them and their correlations. The drastic consequences of such confusion are poor design of curriculum and teaching strategies and a misleading assessment of PEOs and ultimately inaccurate corrective plan [1,35,36]. There are several contributing factors to this confusion, among which the lack of adequate knowledge of these components and their correlations [36,37] and the ambiguity in their terminologies and wordings are the most prominent [29].…”

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

Yahya

Osman

2019

Comp Applic In Engineering

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In outcome‐based academic programs, Program Educational Objectives (PEOs) and Student Outcomes (SOs) are two cores around which all programs’ components and processes revolve. Needless to say, the PEOs‐SOs mapping is very critical for program success and, therefore, a deep understanding of PEOs, SOs, and their intra/inter‐correlations is very important for effective program's decisions making. In this context, this paper proposes a data mining‐based approach to discover hidden knowledge of PEOs, SOs, and their mapping and correlations in engineering programs. More specifically, the proposed approach employs association rule mining techniques to generate association rules, among and between PEOs and SOs, from PEOs‐SOs mapping data of a set of engineering programs, which can be then filtered and manipulated to discover the desired knowledge. To this end, a set of 152 self‐study reports of engineering programs, accredited by American Board for Engineering and Technology‐Engineering Accreditation Commission (ABET‐EAC), are collected and the mapping data between their PEOs and ABET‐EAC SOs are extracted. The dataset is processed and transformed into a representation suitable for association rules mining. This involves developing a set of PEOs labels, annotating data with PEOs labels, and extracting three target datasets. Apriori algorithm is then applied to each dataset to generate three sets of association rules. The generated association rules are then filtered and manipulated to discover the knowledge of PEOs, ABET‐EAC SOs, and their mapping and correlations. Finally, a discussion on the informativeness of the discovered knowledge for the decisions making in engineering education is given.

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confidence: 99%

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confidence: 99%

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

Yahya

Osman

2019

Comp Applic In Engineering

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“…General courses provide core foundational knowledge, and each course in these courses provides a range of activities to enhance students' skills in knowledge, cognitive skills, communication, leadership, teamwork, presentation, technical writing, and psychomotor skills domains. e life cycle of the program includes various phases such as design and speci cation, implementation, and continuous updating as shown by the authors in [1][2][3]. PEOs are speci ed by various stakeholders and components and are de ned as a set of skills that relate to knowledge, skills, and attitudes that learners are likely to demonstrate.…”

Section: Introductionmentioning

confidence: 99%

The Design of Academic Programs Using Rough Set Association Rule Mining

Hogo

2022

Applied Computational Intelligence and Soft Computing

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Program accreditation is important for determining whether or not a program or institution meets quality standards. It helps employers to evaluate the programs and qualifications of their graduates as well as to achieve its strategic goals and its continuous improvement plans. Preparing for accreditation requires extensive effort. One of the required documents is the program’s self-study report (SSR), which includes the PEO-SO map (which allocates the program’s educational objectives (PEOs) to student learning outcomes (SOs)). It influences program structure design, performance monitoring, assessment, and continuous improvement. Professionals in each academic engineering program have designed their PEO-SO maps in accordance with their experiences. The problem with the incorrect design of map design is that the SOs are either missing altogether or cannot be assigned to the correct PEOs. The objective of this work is to use a hybrid data mining approach to design the correct PEO-SO map. The proposed hybrid approach utilizes three different data mining techniques: classification to find the similarities between PEOs, crisp association rules to find the crisp rules for the PEO-SO map, and rough set association rules to find the coarse association rules for the PEO-SO map. The work collected 200 SSRs of accredited engineering programs by the ABET-EAC. The paper presents the different phases of the work, such as data collection and preprocessing, building of three data mining models (classification, crisp association rules, and rough set association rules), and analysis of the results and comparison with related work. The validation of the obtained results by different fifty specialists (from the academic engineering field) and their recommendations were also presented. The comparison with other related works proved the success of the proposed approach to discover the correct PEO-SO maps with higher performance.

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“…It is realized through identifying three types of outcomes: PEOs, PLOs, and Course Outcomes (COs) [4]. Although PEOs describe, in broad statements, career and professional accomplishments that the program is preparing its graduates to achieve, PLOs describe, in narrower statements, the knowledge, describe, in broad statements, career and professional accomplishments that the program is preparing its graduates to achieve, PLOs describe, in narrower statements, the knowledge, skills, and behaviors those students are expected to attain by the time of graduation [5]. Similarly, COs are statements that describe the knowledge, skills, and behaviors students are expected to attain as a result of taking a course.…”

Section: Introductionmentioning

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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Outcome Based Learning: A case study

Cited by 7 publications

References 3 publications

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

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

The Design of Academic Programs Using Rough Set Association Rule Mining

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

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