Pedagogical algorithm visualization technology aims to assist learners in understanding the dynamic behavior of computer algorithms. A key trend in past experimental studies is that learners benefit most when they are actively engaged with algorithm visualization technology. Inspired by this trend, we are exploring the pedagogical value of a novel active learning activity—the Studio Experience—within the context of an introductory CS1 unit on algorithmic problem-solving. In a Studio Experience, student pairs are given algorithm design problems, e.g., “design two alternative algorithms that reverse the values in a list. ” They are tasked both with constructing algorithmic solutions and accompanying visualizations, and with presenting their visualizations for feedback and discussion in a session modeled after an architectural “design crit. ” Through an observational study of studio experience sessions in which students used two alternative forms of visualization technology— art supplies and a computer-based tool—we gained insight into (a) the processes by which students construct visual presentations of algorithms, (b) the characteristics of their visual presentations; (c) the nature of conversations mediated by visual algorithmic solutions; and (d) the kind of visualization technology that best supports these activities. Based on our results, we suggest improvements to the approach, and propose an agenda for future empirical studies.

Algorithm visualization research for computer science education has primarily focused on expert-created visualizations. However, constructionist and situated theories of learning suggest that students should develop and share their own diverse understandings of a concept for deep learning. This paper presents a novel approach to algorithm learning by visualization construction, sharing, and evaluation. Three empirical studies in which students engaged in these activities are discussed. The resulting learning benefits are quantified, and student visualizations are characterized in multiple ways. Then another study that investigated how specific characteristics of such visualizations influence learning is described. This work demonstrates the effectiveness of having students create algorithm visualizations, identifies characteristics of student-created algorithm visualizations and illuminates the learning benefits derived from these characteristics.

This paper describes an experience with use of peer assessment in tutorials as a tool to promote deep learning from early stages of a course on Data Structures and Algorithms. The goal was to improve the utility of tutorials in encouraging more efficient learning habits. Since assessment forms a key part of the actual curriculum, tutorial exercises were for credit, but the emphasis was on formative assessment. The novelty in this approach is that peer assessment has not been extensively studied in Computer Science Education for content of the kind covered in this course. Evaluation is limited by the fact that other details of the course were changed. Two surveys were conducted, one soon after the first assignment, the other soon after the second assignment. Of various aspects of the course surveyed, the tutorial quizzes were the least popular, but improved in popularity between the two surveys. The overall effect based on general observation of the class appeared to be positive. Results were closer to a normal distribution than for the previous 2 years. Performance in the first assignment, which required understanding of how the theory is applied in a practical situation, suggested that deep learning had taken place. 1

While the demand for college graduates with computing skills continues to rise, such skills no longer equate to mere programming skills. Modern day computing jobs demand design, communication, and collaborative work skills as well. Since traditional instructional methods in computing education tend to focus on programming skills, we believe that a fundamental rethinking of computing education is in order. We are exploring a new “studio-based ” pedagogy that actively engages undergraduate students in collaborative, design-oriented learning. Adapted from architectural education, the studio-based instructional model emphasizes learning activities in which students (a) construct personalized solutions to assigned computing problems, and (b) present solutions to their instructors and peers for feedback and discussion within the context of “design crits. ” We describe and motivate the studio-based approach, review previous efforts to apply it to computer science education, and propose an agenda for multi-institutional research into the design and impact of studio-based instructional models. We invite educators to participate in a community of research and practice to advance studio-based learning in computing education.

In this paper, two emerging learning and teaching methods have been studied: collaboration in concert with algorithm visualization. When visualizations have been employed in collaborative learning, collaboration introduces new challenges for the visualization tools. In addition, new theories are needed to guide the development and research of the visualization tools for collaborative learning. We present an empirical study, in which learning materials containing visualizations on different Extended Engagement Taxonomy levels were compared, when students were collaboratively learning concepts related to binary heap. In addition, the students ’ activities during the controlled experimental study were also recorded utilizing a screen capturing software. Pre- and post-tests were used as the test instruments in the experiment. No statistically significant differences were found in the post-test between the randomized groups. However, screen capturing and voice recording revealed that despite the randomization and instructions given to the students, not all of the students performed on the engagement level, to which they were assigned. By regrouping the students based on the monitored behavior, statistically significant differences were found in the total and pair average of the post-test

This paper describes the pedagogical implications of the GREWPtool (the Groupware Research in Education and the Workforce Project), a same time different place groupware tool built to support synchronous, collaborative coding among small to medium sized groups. GREWPtool exploits the educational benefits of paired programming while extending the model to allow students synchronous control of the same code. This effectively drops the constraints of turn taking, and allows for a richer interaction. By recording user activity we were also able to ameliorate the issues of accountability and individual assessment common to group work. The result was a powerful tool that can be used to provide interactive lectures, structure classroom activity, and facilitate paired programming during labs. In this paper we present the design, implementation, and deployment of GREWPtool with an emphasis on how it enhances the classroom experience. In particular, we give a qualitative analysis of how this tool has been used in six small lab courses over the past year. We also discuss the use of the GREWPtool as a platform for studying pair programming and we provide some preliminary findings of a controlled experiment to test the effectiveness of pair programming among novices. 1

Traditi nal le arning c nte nt manage me nt syste ms hav e minimal supp rt f r aware ne ss am ng le arne rs, and te nd t s upp rt nly l se ly c uple d c llab rati n fe ature s. This pape r sh ws h w we ha ve inte grate d use r m de lbase d aware ne ss fe ature s and c llab rati n fe ature s int ur le arning c nte nt manage me nt syste m f r b th le arne rs and instruct rs . 1

Computer science education research has mostly focused on cognitive approaches to learning. Cognitive approaches to understanding learning do not account for all the phenomena observed in teaching and learning. A number of apparently successful educational approaches like peer assessment, apprentice-based learning and action learning have aspects which are not satisfactorily explained by purely cognitive models. On the other hand, these approaches are stratagems rather than comprehensive theories, in that they do not apply in all cases. Education theories which explore learning beyond the cognitive domain such as social construction may provide additional insights into matters like teaching style, curriculum design and assessment practices. This paper proposes a start towards introducing social construction into computer science education, and proposes new directions for research, curriculum development and educational practice. 1

Abstract. We present a collaborative learning tool for programming, Jeliot Collaboratively or JeCo. Jeliot Collaboratively is a combination of a program visualization tool for Java programs, called Jeliot 3, and a collaborative authoring tool, Woven Stories. We introduce these systems and explain how they can be used in learning. Furthermore, we present future directions in order to support a wider range of use cases with JeCo. Key words: CSCL, program visualization, programming learning, collaborative authoring, Java. 1.