Leland L. Beck

Smallest-last ordering and clustering and graph coloring algorithms

Smallest-last vertex ordering and prlonty search are utdlzed to show for any graph G = (IT, E) that the set of all connected subgraphs maxunal with respect to their minimum degree can be determined in O(I EI + I VI) time and 21El + O(I VI) space It is further noted that the smallest-last graph coloring algonthrn can be unplemented in O(I E I + I V[) tune, and particularly effective aspects of the resulting coloring are discussed.

Cooperative learning instructional methods for CS1: Design, implementation, and evaluation

Cooperative learning is a well-known instructional technique that has been applied with a wide variety of subject matter and a broad spectrum of populations. This article briefly reviews the principles of cooperative learning, and describes how these principles were incorporated into a comprehensive set of cooperative learning activities for a CS1 course. In each activity, specific roles are assigned to group members in order to highlight important concepts and to enhance the overall functioning of the group. The group processing is followed by a whole-class debriefing led by the instructor, which works in tandem with the group activity to help students improve their understanding of the material. The effectiveness of these cooperative learning activities was assessed in a series of educational research studies which spanned three academic years and included two different instructors. The results of these studies show statistically significant benefits from the cooperative learning approach, both overall and for a broad range of subgroups of students. The article concludes with suggestions for faculty members who may want to use these cooperative learning activities in the classroom, or to develop their own activities along similar lines.

A project-oriented undergraduate course sequence in software engineering

The design of a two-semester course sequence in software engineering is described. These courses, offered at the undergraduate level, are centered around student projects developed in conjunction with local industry; the projects are used as a focal point to motivate and teach software engineering concepts and tools. The goal of the courses is to provide the student with an overview of the entire software development process, experience as a member of a project team, and exposure to a real-world software environment. This paper describes the course organization and topics, and techniques for project selection and monitoring. Results and experience gained to date with this approach are also discussed.

A dynamic storage allocation technique based on memory residence time

Holland, Amsterdam (1970). 4. Buzen, J.P. Computational algorithms for closed queueing networks with exponential servers. Comm. ACM 16, 9, (Sept. 1973), 527-531. 5. Denning, P.J. and Buzen, J.P. The operational analysis of queueing network models. Computing Surveys, 10, 3 (Sept. 1978), 225-261. 6. Feller, W. An Introduction to Probability Theory and its Applications. Volume I (2nd Ed.), John Wiley and Sons, New York, (1957). 7. Gordon, W. J. and Newell, G. F. Closed queueing systems with exponential servers. Oper. Res., 15, 2 (March 1967) 254-265. 8. Jackson, J.R. Jobshop-like queueing systems. Management Science, 10, 1 (Jan. 1963) 131-142. 9. Kleinrock, Leonard Queueing Systems, Volume 2, WileyInterscience, New York (1976). 10. Lipsky, L. Recent applications of queueing theory to computer modelling. First Annual International Conference on Computer Capacity Management. Institute for Software Engineering, Washington, D.C., pp. 97-141, (April 1979); also SHARE LIII A359, New York, (August 1979). I1. Lipsky, L. Time-sharing systems considered as exponential servers with population size constraints. The Computer Journal, 23, (Sept. 1980), 290-297. 12. Reiser, M. and Kobayashi, H. Queueing networks with multiple closed chains: Theory and computational algorithms. IBM. J. Res. and Dev., 19, 3 (May 1975) 283-294. 13. Scherr, A. A. An Analysis of Time-Sharing Computer Systems. MIT Press, Cambridge, Mass., (1967). 14. Williams, A. C. and Bhandiwad, R. A. A generating function approach to queueing network analysis of multiprogrammed computers Networks, 6, 1 (Jan. 1976) 1-22. O p e r a t i n g Sys t ems A n i t a J o n e s

Alternatives to lecture: revealing the power of peer instruction and cooperative learning

This session will demonstrate best practices for integrating a variety of student engagement pedagogies into the classroom. Presenters play the role of instructors on the first day of class. Audience members play the role of students as they collaborate on a variety of sample activities. By providing models of active learning in computing contexts, we seek to motivate instructors to adopt these pedagogies in their classrooms. Attendees will be invited to join a growing community of faculty who share resources and best practices. Sufficient time will be reserved for questions and discussion.

Workshop Applying Cooperative Learning Methods in Teaching Computer Programming

This workshop examines how to use cooperative learning as an effective instructional strategy for teaching computer programming. After an experiential introduction and a brief presentation of theoretical principles, participants work collaboratively to design and evaluate instructional activities of their own. Participants receive a set of cooperative learning activities that they can incorporate into their own programming courses, including guidance and suggestions for applying these exercises in the classroom

Applying Cooperative Learning Methods in Teaching Computer Programming

1 Leland L. Beck, Department of Computer Science, San Diego State University, beck@cs.sdsu.edu 2 Alexander W. Chizhik, School of Teacher Education, San Diego State University, achizhik@mail.sdsu.edu Abstract This workshop examines how to use cooperative learning as an effective instructional strategy for teaching computer programming. After an experiential introduction and a brief presentation of theoretical principles, participants will work collaboratively to design and evaluate instructional activities of their own. Participants will also receive a set of cooperative learning activities that they can incorporate into their own programming courses, including guidance and suggestions for applying these exercises in the classroom.