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Dispositions are valued by employers and promoted by recent computing curricular recommendations. Yet, fostering and assessing dispositions are not well understood. In a multi-institutional study, students were asked to assess their dispositions in terms of behaviors that were identified in prior literature for those dispositions, both at the start and the end of a term. During the term, instruments were used to have students reflect on their dispositions. The research questions of the study are: 1) Do students associate behaviors with the dispositions for which they were identified in prior work? 2) Does reflecting on dispositions change how students assess themselves in terms of the behaviors? and 3) Is there a difference between introductory and upper-level students in how they assess themselves in terms of the behaviors? The findings of the study are that 1) at least 60% of the students associated the behavior statements with the dispositions for which they were identified; 2) students lowered their self-assessment of some behaviors after reflecting on dispositions; and 3) upper-level students assessed themselves more positively on some behaviors than introductory students. These results support a model of development of dispositions in which self-assessment of behaviors associated with dispositions improves with academic level, but at each level, gets revised lower after reflection. 

Lifelong learning is essential in computing, given the dynamic nature of the field. Employers and curricular reviewers recognize the value of being self-directed in support of becoming a lifelong learner. The ACM/IEEE-CS Computing Curricula 2020 report identifies self-directed as having elements of self-motivation, determination, and independence. Little is known, however, about how to cultivate this disposition in computing courses. The motivation of this study is to better understand what behaviors computing students believe are self-directed. This study’s research questions are: 1) What do students describe as their self-directed practices in computing? and 2) What do students report are factors that prevent them from being self-directed? Assignments in five undergraduate computing courses from four institutions included prompts to elicit student’s reflections on how they were self-directed (or not). Thematic content analysis using the constant comparative method produced eight categories of self-directed behaviors (utilizing external resources, learning necessary material, working independently, assessing oneself, planning ahead, applying useful techniques, completing the assigned work, and reviewing against expectations). Thematic analysis also resulted in five categories of factors that impeded the self-directed behavior (assignment structure, unsuccessful effort, self-sufficiency, insufficient motivation, and insufficient time). Understanding how students describe self-directedness can help educators design pedagogical and assessment approaches that facilitate self-directed student behaviors in the classroom. 

Dispositions, skills, and knowledge form the three components of competency-based education. Moreover, dispositions are considered crucial for students to succeed in the workplace. Few studies investigate how dispositions manifest in the form of observable behaviors, which causes challenges for both students and educators. Computing students, for example, may not understand what is expected of them, and how to achieve dispositions. This paper presents the results of a qualitative, multi-institutional study on students’ understanding of the dispositions adaptable, persistent, self-directed, meticulous, and professional. Perceptions were gathered by asking for exemplary situations of students applying each of the five dispositions in the context of assignments within computing courses. Students who indicated they did not apply the disposition were asked to describe the hindering circumstances. The data was evaluated by using Mayring’s content analysis technique, resulting in the development of deductive-inductive categories of observable behaviors reflecting the student’s perspective. For meticulous and professional, new categories representing observable behaviors were developed. For adaptable, persistent, and self-directed, the authors confirmed and extended prior work. Moreover, factors hindering students in applying the investigated dispositions are identified. The resulting categories with observable student behaviors are an important step toward the operationalization of competency-based learning outcomes including dispositions. A common understanding of dispositions will also help with the design of new forms of instruction and measures to foster the application of dispositions in the context of computing education. 

Dispositions, along with skills and knowledge, form the three components of competency-based education. Moreover, studies have shown dispositions to be necessary for a successful career. However, unlike evidence-based teaching and learning approaches for knowledge acquisition and skill development, few studies focus on translating dispositions into observable behavioral patterns. An operationalization of dispositions, however, is crucial for students to understand and achieve respective learning outcomes in computing courses. This paper describes a multi-institutional study investigating students’ understanding of dispositions in terms of their behaviors while completing coursework. Students in six computing courses at four different institutions filled out a survey describing an instance of applying each of the five surveyed dispositions (adaptable, collaborative, persistent, responsible, and self-directed) in the courses’ assignments. The authors evaluated data by using Mayring’s qualitative content analysis. The result was a coding scheme with categories summarizing students’ concepts of dispositions and how they see themselves applying dispositions in the context of computing. These results are a first step in understanding dispositions in computing education and how they manifest in student behavior. This research has implications for educators developing new pedagogical approaches to promote and facilitate dispositions. Moreover, the operationalized behaviors constitute a starting point for new assessment strategies of dispositions. 

Dispositions are cultivable behaviors desirable in the workplace. Examples of dispositions are being adaptable, meticulous, and self-directed. The eleven dispositions described in the CC2020 report should not be confused with the professional knowledge of computing topics, or with skills, including technical skills, along with cross-disciplinary skills such as critical thinking, problem-solving, teamwork, or communication. Dispositions, more inherent to human characteristics, identify personal qualities and behavioral patterns important for successful professional careers. The leaders of this special session collaborate on a multi-institutional project funded by the National Science Foundation. Using their experiences at four higher education institutions, they will demonstrate how to foster dispositions among computing students through two hands-on activities. The audience will get first-hand experience using reflection exercises and vignettes, and will participate in debating their design, merits, and limitations. The resulting interaction will provide the audience ample time to discuss the benefits and challenges of incorporating and fostering dispositions in computing programs. It is hoped that participants will leave with concrete ideas on how to extend the current work to their own courses, programs, and institutions.