The Impact of Appropriate Planning and Robust Evaluation Strategies on Continuous Improvement of Student Learning

The Impact of Appropriate Planning and Robust Evaluation Strategies on Continuous Improvement of Student Learning

Imtiaz Hussain Khan (Computer Science Department, King Abdulaziz University, Saudi Arabia)
Copyright: © 2020 |Pages: 18
DOI: 10.4018/IJOPCD.2020070102
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Abstract

This paper discusses a unified framework for systematic assessment and evaluation of student outcomes for a modern computer science (undergraduate) program seeking ABET accreditation. The framework uses a carefully designed sampling plan, informed by robust analysis and experiences over more than five years, to evaluate and assess student outcomes. The proposed framework is expected to enhance student learning and faculty satisfaction. The effectiveness of the proposed framework is validated using direct and indirect assessment data gathered from different stakeholders. Moreover, two consecutive, full six-years each, ABET accreditations (2013-2019 and 2019-2025) ascertain the robustness of the proposed framework.
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1. Introduction

Quality assurance of an educational program largely depends on effective assessment and evaluation processes pertaining to monitoring students' performance (Popham, 1999; Braun, Kanjee, Bettinger, & Kremer, 2006; Jin, 2010; Barney, Khurum, Petersen, Unterkalmsteiner, & Jabangwe, 2012; Crilly & Hartnett, 2015; Wimmer, Powell, Kilgus, & Force, 2017; Chou, 2018). Program reviews are generally challenged to meet a variety of criteria as set by an accreditation body. Successful program reviews are thus achieved using appropriate assessment and evaluation plans. As the world has witnessed many revolutions in the education sector since the dawn of the 20th century, the focus of accreditation processes of educational programs has also been shifted towards outcome-based assessment and evaluation. This departure first required a need to identify a set of characteristics for which an educational program intends to prepare its students. In Accreditation Board for Engineering and Technology (ABET) (ABET, 2019) parlance, these characteristics are attributed as student outcomes (henceforth, SOs), which require a robust mechanism for their successful evaluation (Somervell, 2015). More specifically, SOs describe “what students are expected to know and be able to do by the time of graduation. These relate to the knowledge, skills, and behaviors that students acquire as they progress through the program” (ABET, 2019). It has been argued that a program's characteristics must be systematically connected to its outcomes (König, Blömeke, Paine, Schmidt, & Hsieh, 2011; König, Ligtvoet, Klemenz, & Rothland, 2017). The program that is the subject of this paper has adopted eleven SOs from ABET (ABET, 2019), nine of which are general-criteria outcomes for computing (namely outcomes, a-i) and two are program-specific-criteria outcomes (namely, j and k). These SOs have further been classified in terms of the type of skills they depict as per ABET guidelines: a, b, c, i, j and k focus on technical or “hardˮ skills, while d-h focus on “soft” skills (Smerdon, 2000; Shuman, Besterfield-Sarce, & McGourty, 2005). The hard-skills related SOs focus on problem solving and computing applications and they can be trustfully assessed from most technical courses. However, the soft-skills related SOs pertaining to, for example, life-long learning and ethics are harder to assess (Shuman, Besterfield-Sarce, & McGourty, 2005; Danaher, Schoepp, & Kranov, 2016).

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