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Closed laboratories are becoming an increasingly popular approach to teaching introductory computer science courses, as they facilitate structured problem-solving and cooperation. However, most closed laboratories have been designed and implemented without embedded instructional research components for constant evaluation of the laboratories' effectiveness. As a result, it is not convenient to maintain and improve the laboratories over time so that they adapt to changing CS topics, curricula, and student needs. This article reports on an integrated framework for designing, implementing, and maintaining laboratories with embedded instructional research design. Although the activities reported here are part of our department-wide effort to cover CS0, CS1, and CS2, we focus here on the design and implementation of the labs for CS1.

Contribution: This article presents a synthesis of the findings and implications from the IC2Think program of research in undergraduate computer science (CS) courses examining student motivation and self-regulated learning (SRL). These studies illuminate both the difficulty and potential for motivating CS students, as well as the uniqueness of CS as a context for studying undergraduate motivation. Background: Computing disciplines are increasingly important in preparing the future workforce. It is imperative that CS educators understand how to motivate students and enhance student outcomes. Synthesizing findings across multiple studies allows for the emergence of new insights into student motivation and SRL. Research Questions: Which aspects of students’ motivation and SRL are predictive of achievement and retention in CS and how can findings inform CS education? Methodology: The primary methodology is a comprehensive review of seven years of research on undergraduate CS education. Studies use a variety of analysis techniques, examine a range of constructs, and include multiple introductory and advanced CS courses. Studies of relationships between variables and change over time were conducted. Findings: The present synthesis of studies on motivation and SRL highlights the complex, counter-intuitive, and positive aspects of student motivation in CS. [ABSTRACT FROM AUTHOR]

Contribution: This paper provides evidence that computational creativity exercises (CCEs) can increase engineering students’ learning in introductory computer science (CS1) courses. Its main contribution is its more rigorous treatment/control group research design that allows testing for causal influences of CCEs on student learning and performance. Background: Computer science (CS) courses are critical foundational courses for engineering students. CCEs that merge computational and creative thinking have been shown to increase achievement and learning of engineering and nonengineering students in CS1 courses, but previous research has used quasi- and non-experimental designs. Intended Outcomes: CCEs are intended to improve students’ learning of CS1 content and problem-solving ability by fostering computational creativity. Application Design: CCEs can improve student learning and can be used to supplement other evidence-based instructional practices. Findings: Propensity score matching was used to create equivalent treatment and control groups; results show that students in the CCE implementation section had higher scores on a CS knowledge test than students in the control section, but not higher self-efficacy for their CS knowledge. Focus group and open-ended survey questions indicated that students had mixed reactions to the CCEs, with about half the students seeing them as improving their learning, understanding, and ability to apply CS in their engineering field. Responses also reinforced the importance of fully incorporating CCEs in courses and aligning them with course topics. [ABSTRACT FROM AUTHOR]

Computational thinking and creative thinking are valuable tools both within and outside of computer science (CS). The goal of the project discussed here is to increase students’ achievement in CS courses through a series of computational creativity exercises (CCEs). In this paper, the framework of CCEs is described, and the results of two separate studies on their impact on student achievement are presented. Students in introductory CS courses completed CCEs as part of those courses. Students in Study 1 came from a variety of programs, and students in Study 2 were engineering majors. A profiling approach was used to test whether the impact of the CCEs could be accounted for by differences in students’ motivated and self-regulated engagement. Overall, CCEs had positive impacts on students’ grades and knowledge test scores, and although there were differences in achievement across the profiles, the impact of the CCEs was generally consistent across profiles. The CCEs appear to be a promising way to increase student achievement in introductory CS courses. Implications and directions for future research are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of suboptimal behavior during online partially observable Markov decision process (POMDP) planning caused by time constraints on planning. Taking inspiration from the related field of reinforcement learning (RL), our solution is to shape the agent's reward function in order to lead the agent to large future rewards without having to spend as much time explicitly estimating cumulative future rewards, enabling the agent to save time to improve the breadth planning and build higher quality plans. Specifically, we extend potential-based reward shaping (PBRS) from RL to online POMDP planning. In our extension, information about belief states is added to the function optimized by the agent during planning. This information provides hints of where the agent might find high future rewards beyond its planning horizon, and thus achieve greater cumulative rewards. We develop novel potential functions measuring information useful to agent metareasoning in POMDPs (reflecting on agent knowledge and/or histories of experience with the environment), theoretically prove several important properties and benefits of using PBRS for online POMDP planning, and empirically demonstrate these results in a range of classic benchmark POMDP planning problems. [ABSTRACT FROM AUTHOR]

Survey informatics leverages two separate but vital areas of research--survey methodology and computer science and engineering--to advance the state of the art in each and improve our understanding of the human experience. [ABSTRACT FROM AUTHOR]

A national web-based survey was administered to 700 undergraduate computer science (CS) programs in the United States as part of a stratified random sample of 797 undergraduate CS programs. The 251 program responses (36% response rate) regarding social and professional issues are presented. This article describes the demographics of the respondents, presents results concerning whether programs teach social and professional issues, how social and professional issues are integrated, perceptions of CS faculty regarding the importance of social and professional issues, pedagogies used to teach social and professional issues, and what specific social and professional topics have been included in the CS curriculum. Additionally, we (a) provide suggestions for CS programs regarding the integration of social and professional issues into the CS curriculum, (b) suggest ways to encourage more social and professional coverage in CS programs, pedagogy, and (c) recommend what social and professional topics should be included in future CS curriculum reports. [ABSTRACT FROM AUTHOR]

Recent years have seen a surge in the use of intelligent computer-supported collaborative learning (CSCL) tools for improving student learning in traditional classrooms. However, adopting such a CSCL tool in a classroom still requires the teacher to develop (or decide on which to adopt) the CSCL tool and the CSCL script, design the relevant pedagogical aspects (i.e., the learning objectives, assessment method, etc.) to overcome the associated challenges (e.g., free riding, student assessment, forming student groups that improve student learning, etc). We have used a multiagent-based system to develop a CSCL application and multiagent-frameworks to form student groups that improve student collaborative learning. In this paper, we describe the contexts of our three generations of CSCL applications (i.e., I-MINDS and ClassroomWiki) and provide a set of lessons learned from our deployments in terms of the script, tool, and pedagogical aspects of using CSCL. We believe that our lessons would allow 1) the instructors and students to use intelligent CSCL applications more effectively and efficiently, and help to improve the design of such systems, and 2) the researchers to gain additional insights into the impact of collaborative learning theories when they are applied to real-world classrooms. [ABSTRACT FROM PUBLISHER]

This study investigated introductory computer science (CS1) students’ implicit beliefs of intelligence. Referencing Dweck and Leggett’s (1988) framework for implicit beliefs of intelligence, we examined how (1) students’ implicit beliefs changed over the course of a semester, (2) these changes differed as a function of course enrollment and students’ motivated self-regulated engagement profile, and (3) implicit beliefs predicted student learning based on standardized course grades and performance on a computational thinking knowledge test. For all students, there were significant increases in entity beliefs and significant decreases in incremental beliefs across the semester. However, examination of effect sizes suggests that significant findings for change across time were driven by changes in specific subpopulations of students. Moreover, results showed that students endorsed incremental belief more strongly than entity belief at both the beginning and end of the semester. Furthermore, the magnitude of changes differed based on students’ motivated self-regulated engagement profiles. Additionally, students’ achievement outcomes were weakly predicted by their implicit beliefs of intelligence. Finally, results showed that the relationship between changes in implicit intelligence beliefs and student achievement varied across different CS1 courses. Theoretical implications for implicit intelligence beliefs and recommendations for STEM educators are discussed. [ABSTRACT FROM AUTHOR]