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Employers seek recruits who can apply the knowledge, skill, and culture they acquire in college to solve problems as soon as they enter the workforce. 

This Innovative Practice Full Paper addresses the assessment of dispositions which, along with knowledge and skills, form the three legs of competency needed to perform a task in context, as described in recent computing curricular reports, particularly ACM/IEEE Computing Curricula 2020 (CC2020). Here, dispositions in CC2020 express the behavioral characteristics of competence, such as being adaptable, collaborative, or inventive. Instructors have assessed knowledge from the start of computing programs and have paid increased attention to assessing skills in recent decades. However, dispositions and their role within competency is relatively new, with little guidance available for assessing dispositions. Lately, computing instructors have begun to understand the importance of evaluating dispositions during the performance of tasks in the real world or in the context of the industry-based global Skills Framework for the Information Age (SFIA). Hence, this paper develops a criterion-based approach for use by educators in assessing competence based on a reflective portfolio of "real-world" achievements. Building on concepts developed by the UK Institute of Coding and other recent reports, this work demonstrates how this assessment approach relates to industry-based competency frameworks such as SFIA and the European e-Competence Framework (eCF). The paper also explores using the criterion-based approach in a classroom environment to help students focus on particular dispositions. Its main contribution is to advance the competency focus in academic computing programs and future computing curricula. 

Program accreditation in medical or religious professions has existed since the 1800s while accreditation of business and engineering programs started in the early twentieth century. With this long history, these disciplines have focused on ensuring the competence of their graduates, as modern society demands appropriate expertise from doctors and engineers before letting them practice their profession. In computing, however, professional accreditation started in the last decades of the twentieth century only after computer science, informatics, and information systems programs became widespread. At the same time, although competency-based learning has existed for centuries, its growth in computing is relatively new, resulting from recent curricular reports such as Computing Curricula 2020, which have defined competency comprising knowledge, skills, and dispositions. In addition, demands are being placed on university programs to ensure their graduates are ready to enter and sustain employment in the computing profession. This work explores the role of accreditation in forming and developing professional competency in non-computing disciplines worldwide, building on this understanding to see how computing accreditation bodies could play a similar role in computing. This work explores the role of accreditation in forming and developing professional competency in non-computing disciplines worldwide, building on this understanding to see how computing accreditation bodies could play a similar role in computing. Its recommendations are to incorporate competencies in all computing programs and future curricular guidelines; create competency-based models for computing programs; involve industry in identifying workplace competencies, and ensure accreditation bodies include competencies and the assessment in their standards. 

This is a panel presentation on the role of dispositions in computing education. 

In the past decade, academic computing curricular guidelines have shifted from specifying knowledge and occasionally technical skills to establishing the overall competence expected of graduates. For instance, Computing Curricula 2020 (CC2020) guidelines identify competency as knowledge, skills, and dispositions where “dispositions” correspond to the behavioral and professional characteristics driven by employer needs and captured by industry-driven frameworks, such as the Skills Framework for the Information Age (SFIA). Computing programs thus must also ensure that graduates have these characteristics to improve initial employment and long-term career prospects. This paper aims to understand and achieve consistency between academia and industry curricular frameworks. The CC2020 dispositions map to the responsibility characteristics for SFIA Level 3, the level appropriate for a new graduate. As the mapping is not one-to-one, the paper reviews the extent to which each SFIA responsibility characteristic requires and enables the CC22020 dispositions, identifying potential shortcomings and, conversely, the importance of each disposition as it supports the responsibility characteristics. The developed mapping is validated by relating the CC2020 dispositions to the SFIA behavioral factors, the principal “21st Century Skills,” and relevant competency-based educational frameworks. Thus, dispositions in competency-focused curricula map to the actual competencies sought by employers. Finally, the paper postulates that future computing curricula must further develop the CC2020 dispositions and relate them to SFIA to guide academic programs in their preparation of career-ready graduates to reduce the current “skills gap”. 

Since the early 21st century, ABET’s accreditation criteria have focused on learning outcomes (what students learn) rather than what professors teach. Such accreditation criteria bring to bear the need for programs to establish clear learning objectives and assessment processes that ensure that program graduates have the requisite technical and professional preparation. To this end, ABET defines student outcomes as “what students are expected to know and be able to do by the time of graduation,” further noting that these outcomes “relate to the knowledge, skills, and behaviors that students acquire as they progress through the program.” With the recent release of Computing Curricula 2020 (CC2020), the competencies of computing program graduates have received additional attention. CC2020 describes competency as “comprising knowledge, skills, and dispositions that are observable in accomplishing a task within a work context.” ABET’s student outcomes thus largely correspond to the CC2020 competencies of program graduates. This paper is a first attempt to reconcile the two notions in the context of computer science. It presents the relevant background and discusses student competencies and their assessments that focus on competency-based learning in computer science. The contributions of this paper are (1) forging an improved shared understanding of computing competencies and (2) an interpretation of ABET’s student outcomes to improve the competency, including dispositions, expectations of computer science graduates. 

A Joint Task Force of the Association for Computing Machinery (ACM), the Institute of Electrical and Electronics Engineers - Computer Society (IEEE-CS), and Association for the Advancement of Artificial Intelligence (AAAI) was constituted in early 2021 to begin the decennial process of revising the Computer Science curricular guidelines, which were last released as Computer Science Curricula 2013 (CS2013). This special session will present the first draft of the revised curricular guidelines, currently referred to as CS202X, and solicit feedback. The CS202X draft will include revisions to CS2013 Knowledge Areas, a proposed competency model being incorporated into the curricular guidelines, and other updates. Targeted towards educators, administrators and others interested in Computer Science curricular issues, this session will be led by the co-chairs and members of the CS202X Steering Committee as part of their process to engage the community and solicit feedback. 

Competency-based learning has been a successful pedagogical approach for centuries, but only recently has it gained traction within computing. Competencies, as defined in Computing Curricula 2020, comprise knowledge, skills, and professional dispositions. Building on recent developments in competency and computing education, this working group examined relevant pedagogical theories, investigates various skill frameworks, reviewed competencies and standard practices in other professional disciplines such as medicine and law. It also investigated the integrative nature of content knowledge, skills, and professional dispositions in defining professional competencies in computing education. In addition, the group explored appropriate pedagogies and competency assessment approaches. It also developed guidelines for evaluating student achievement against relevant professional competency frameworks and explores partnering with employers to offer students genuine professional experience. Finally, possible challenges and opportunities in moving from traditional knowledge-based to competency-based education were also examined. This report makes recommendations to inspire educators of future computing professionals and smooth students’ transition from academia to employment. 

Competency-based learning has been a successful pedagogical approach for centuries, but only recently has it gained traction within computing education. Building on recent developments in the field, this working group will explore competency-based learning from practical considerations and show how it benefits computing. In particular, the group will identify existing computing competencies and provide a pathway to generate competencies usable in the field. The working group will also investigate appropriate assessment approaches, provide guidelines for evaluating student attainment, and show how accrediting agencies can use these techniques to assess the level of competence reflected in their standards and criteria. Recommendations from the working group report are intended to help practical computing education writ large. 

As data science is an evolving field, existing definitions reflect this uncertainty with overloaded terms and inconsistency. As a result of the field’s fluidity, there is often a mismatch between what data-related programs teach, what employers expect, and the actual tasks data scientists are performing. In addition, the tools available to data scientists are not necessarily the tools being taught; textbooks do not seem to meet curricular needs; and empirical evidence does not seem to support existing program design. Currently, the field appears to be bifurcating into data science (DS) and data engineering (DE), with specific but overlapping roles in the combined data science and engineering (DSE) lifecycle. However, curriculum design has not yet caught up to this evolution. This working group report shows an empirical and data-driven view of the data-related education landscape, and includes several recommendations for both academia and industry that are based on this analysis.