Graduate Attribute Assessment in Software Engineering Program at University of Ottawa – Continual Improvement Process

George, Aneta; Lethbridge, Timothy C.; Peyton, Liam

doi:10.24908/pceea.v0i0.6484

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…There are also several tools and applications used in the evaluation process of learning outcomes in today's market. Researchers in (George et al, 2016) present a developed tool that supports performance management of graduate attributes. The tool is used to simplify data collection, improves visualization of results and provides a flexibly to integrate internal indicators or external indicators.…”

Section: Background and Related Workmentioning

confidence: 99%

Model-Driven Framework for Evaluating Learning Outcomes Process

Alhaj¹,

Sharah²

2020

Journal of Computer Science

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Evaluating learning outcomes in academic institution can be complex and challenging. Several quantitative and qualitative assessment approaches have been adopted to enhance the process of managing, measuring and visualizing the learning outcomes. The difficulty of implementing and analyzing the evaluation process is mainly caused by the nature of the raw data used in assessment. The data is usually unstructured, complex, text-heavy and collected in high volumes. It may also be extracted from heterogeneous platforms and require privileged accessibility. Using paper-based assessment, such as rubric, in complex evaluation process may cause error prone, confusion in analyzing the learning outcomes and subject to different interpretations of the assessment by academic constituencies. In this study, we propose a model-driven framework for evaluation process of the learning outcomes. The framework has four activities: The data collection and data processing activities are used to extract complex data into a useful information for assessment. The model-driven assessment activity is used to generate and analyze goal models of the learning outcomes in a formal way and allows the assessment at different level of academic institutions. Finally, the evaluation reporting activity is used to generate reports that summarizes the institutional status, metrics and real-time data in a form visual object. A prototype implementation of the framework is evaluated using a case study of an ongoing project at Al-Ahliyya Amman University.

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Section: Background and Related Workmentioning

confidence: 99%

Model-Driven Framework for Evaluating Learning Outcomes Process

Alhaj¹,

Sharah²

2020

Journal of Computer Science

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“…The CEAB mandate has been described as a data science problem because of its potential to generate overwhelming amounts of data and necessitate streamlined workflows for users [66]. The process involves many users [67] and massive amounts of data to be collected, visualized, analyzed, interpreted, discussed, and implemented into program improvement [68,69,70]. Mathematically, the number of attributes, sub-attributes, programs, courses, and students could result in more than one million data entries each year at a single institution [30].…”

Section: Software Toolsmentioning

confidence: 99%

“…In the absence of any comprehensive, turnkey solution, institutions have been developing their own in-house tools that combine existing applications [66]-for example: the Integrated Course Design Tool (ICDT) at the University of Calgary [68]; the Graduate Attribute Information Analysis (GAIA) system at the University of Ottawa [69]; and the Outcome-Based Analytics and Continuous Improvement System (OBACIS) at the University of Regina [67]. The efficiency and sustainability of these ad hoc processes is questionable [66].…”

Section: Software Toolsmentioning

confidence: 99%

Approaches to Graduate Attributes and Continual Improvement Processes in Faculties of Engineering Across Canada: A Narrative Review of the Literature

Parker

Watson

Ivey

et al. 2019

PCEEA

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The CEAB accreditation requirement of graduate attributes and continual improvement processes (GACIP) has been a pervasive topic in the annual CEEA conference proceedings since 2010. The proceedings are a rich primary source of work being done in Canadian tertiary institutions. This narrative review of the literature consolidates and discusses the relevant CEEA papers for 2010-2017 in a manner that is useful to leadership and decision-makers at accredited faculties of Engineering nationwide. Four guiding research questions were asked of this literature: (1) What general frameworks are being implemented as accredited faculties of Engineering across Canada approach GACIP?; (2) What are the specific activities and methods of one or more of the GACIP steps?; (3) What are the roles and responsibilities of people involved?; and (4) What perspectives are taken in response to the CEAB accreditation criteria, including concerns, issues, and benefits? A qualitative content analysis was conducted on 106 papers meeting selection criteria. Emergent topics were used to form the discussion.

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“…The Graduate Attribute Information Analysis system (GAIA) was developed at the University of Ottawa to support data collection and performance management of graduate attributes for engineering programs at the program level and at the course level [10]. This paper reports on our research to develop support for cohort analysis and reporting.…”

Section: Introductionmentioning

confidence: 99%

Cohort Analysis and Reporting for Graduate Attribute Assessment

George¹,

Peyton²

2018

PCEEA

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The Graduate Attribute Information Analysis system (GAIA) was developed at the University of Ottawa to support data collection and performance management of graduate attributes for engineering programs at the program level and at the course level [10]. This paper reports on our research to develop support for cohort analysis and reporting by providing a single consistent view of graduate attributes (GA) and performance indicators for groups of students who started and finished an engineering program at the same time. This is supported by two special purpose reports: Graduate Attribute Report per Cohort (GAR/C) and Course Progression Report per Cohort (CPR/C). The former shows average GA data per attribute, the latter tracks student achievement as students progress in their program. It also adds to the historic data trend analysis for a program. Furthermore, a COOP Progress Report per cohort (COOPR/C) is generated.

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Graduate Attribute Assessment in Software Engineering Program at University of Ottawa – Continual Improvement Process

Abstract: And we support measurement-specific range boundaries to better match the expected level of knowledge students must achieve.

Cited by 6 publications

References 10 publications

Model-Driven Framework for Evaluating Learning Outcomes Process

Model-Driven Framework for Evaluating Learning Outcomes Process

Approaches to Graduate Attributes and Continual Improvement Processes in Faculties of Engineering Across Canada: A Narrative Review of the Literature

Cohort Analysis and Reporting for Graduate Attribute Assessment

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