ACM SIGCSE Bulletin, Volume 35

Volume 35, Number 1, January 2003

Volume 35, Number 2, June 2003

• Mark Guzdial, Elliot Soloway:
  Computer science is more important than calculus: the challenge of living up to our potential. 5-8
  

• C. Dianne Martin:
  Computing curricula 2001: reverse engineering a computer science curriculum (part 2). 9-10
  

• John A. N. Lee:
  "Lee's law". 10-12
  

• Tony Clear:
  Documentation and agile methods: striking a balance. 12-13
  

• John T. Gorgone:
  Information technology accreditation criteria. 13-15
  

• Raymond Lister:
  A research manifesto, and the relevance of phenomenography. 15-16
  

• Renée McCauley:
  Resources for teaching and learning about human-computer interaction. 16-17
  

• Janet Hartman:
  Corporate clips. 17-19
  

• Henry MacKay Walker:
  Lessons from the CUPM. 19-21
  

• Judith L. Gersting, Frank H. Young:
  Treating our students as adults. 21-22
  

• P. G. Thomas:
  Examinations in computing over the Internet. 22-24
  

• Robert D. Campbell:
  Cybersecurity. 24-26
  

• Jeffrey Popyack:
  Scholarship, student awards, microbreweries, and baseball. 26-27
  

• Peter B. Henderson:
  Inductive reasoning. 27-29
  

• David Ginat:
  Sorting and disorders. 29-30
  

• Nick Parlante:
  Platonic Euler solids. 30-31
  

• Carlos Iván Chesñevar, Maria Laura Cobo, William Yurcik:
  Using theoretical computer simulators for formal languages and automata theory. 33-37
  
• Orit Hazzan:
  Application of computer science ideas to the presentation of mathematical theorems and proofs. 38-42
  
• Ranjan Chaudhuri:
  Do the arithmetic operations really execute in constant time? 43-44
  
• Trudy Howles:
  Fostering the growth of a software quality culture. 45-47
  
• John F. Dooley:
  Software engineering in the liberal arts: combining theory and practice. 48-51
  
• Louise E. Moses:
  Design issues in the visual era. 52-56
  
• Michael A. Wirth:
  E-notes: using electronic lecture notes to support active learning in computer science. 57-60
  
• Paula Gabbert:
  Globalization and the computing curriculum. 61-65
  
• Lisa Jamba-Joyner, William Klostermeyer:
  Predictors for success in a discrete math course. 66-69
  
• Kent White:
  A comprehensive CMPS II semester project. 70-73
  
• Ross Grable:
  Information characteristics for the curriculum. 74-77
  
• Nelishia Pillay:
  Developing intelligent programming tutors for novice programmers. 78-82
  
• Michaelangelo Salcedo:
  Faculty and the 21^st century student in USA higher education. 83-87
  
• Jesse M. Heines:
  Enabling XML storage from Java applets in a GUI programming course. 88-93
  
• Andrew T. Phillips, C. Alex Buerkle:
  A computational science case study: classification of hybrids using genetic markers and maximum-likelihood estimates. 94-98
  
• Stephen P. Carl:
  The treatment of deep vs. shallow copy in introductory C++ textbooks. 99-102
  
• Jonathan P. Bernick:
  The Flo-and-Mac problem: a tool for encouraging undergraduate research. 103-106
  
• Mithun Acharya, Robert Funderlic:
  'Laurel and Hardy' model for analyzing process synchronization algorithms and primitives. 107-110
  
• Philip J. Burton, Russel E. Bruhn:
  Teaching programming in the OOP era. 111-114
  
• Torben Lorenzen:
  The reverse trace: a programming tool. 115-116
  
• Timothy J. Rolfe:
  Spreadsheet-aided numerical experimentation: analytic formula for Fibonacci numbers. 117-119
  
• John Mason:
  Comments considered harmful. 120-122
  

• Thomas L. Naps, Guido Rößling, Vicki L. Almstrum, Wanda Dann, Rudolf Fleischer, Christopher D. Hundhausen, Ari Korhonen, Lauri Malmi, Myles F. McNally, Susan H. Rodger, J. Ángel Velázquez-Iturbide:
  Exploring the role of visualization and engagement in computer science education. 131-152
  
• John P. Dougherty, Tom Dececchi, Tony Clear, Brad Richards, Stephen Cooper, Tadeusz Wilusz:
  Information technology fluency in practice. 153-171
  
• Martin Dick, Judy Sheard, Catherine C. Bareiss, Janet Carter, Donald Joyce, Trevor Harding, Cary Laxer:
  Addressing student cheating: definitions and solutions. 172-184
  
• Peter B. Henderson, Lewis E. Hitchner, Jane Fritz, Bill Marion, Christelle Scharff, John Hamer, Charles Riedesel:
  Materials development in support of mathematical thinking. 185-190
  
• Pamela B. Lawhead, Michaele E. Duncan, Constance G. Bland, Michael Goldweber, Madeleine Schep, David J. Barnes, Ralph G. Hollingsworth:
  A road map for teaching introductory programming using LEGOcopyright mindstorms robots. 191-201
  

Volume 35, Number 3, September 2003

Volume 35, Number 4, December 2003

• Christos H. Papadimitriou:
  MythematiCS: in praise of storytelling in the teaching of computer science and math. 7-9
  

• Don Gotterbarn:
  Injectable computers: once more into the breach! the life cycle of computer ethics awareness. 10-11
  

• Michael R. Williams:
  The computer history museum. 12-13
  

• Tony Clear:
  The waterfall is dead..: long live the waterfall!! 13-14
  

• John T. Gorgone:
  ABET's general accreditation criteria to apply to all computing programs. 14-16
  

• Raymond Lister:
  The five orders of teaching ignorance. 16-17
  

• Renée McCauley:
  Rubrics as assessment guides. 17-18
  

• Henry MacKay Walker:
  Do computer games have a role in the computing classroom? 18-20
  

• Robert D. Campbell:
  ACM two-year college education committee report. 20-21
  

• Jeffrey Popyack:
  Scholarships, awards, advice, and the abacus. 21-23
  

• Peter B. Henderson:
  More on inductive reasoning. 23-25
  

• David Ginat:
  Board reconstruction. 25-26
  

• Nick Parlante:
  Astrachan's law. 26-27
  

• Tami Lapidot, Orit Hazzan:
  Methods of teaching a computer science course for prospective teachers. 29-34
  
• Jacqueline Wong, Timon Du:
  Project-centered teaching on CBIS to IBBA students in Hong Kong. 35-38
  
• Juan Manuel Dodero, Camino Fernández, Daniel Sanz:
  An experience on students' participation in blended vs. online styles of learning. 39-42
  
• Carol Traynor, Maria McKenna:
  Service learning models connecting computer science to the community. 43-46
  
• Faith Clarke, Han Reichgelt:
  The importance of explicitly stating educational objectives in computer science curricula. 47-50
  
• Theresa Beaubouef:
  Why computer science students need language. 51-54
  
• Cindy H. Randall, Barbara Price, Han Reichgelt:
  Women in computing programs: does the incredible shrinking pipeline apply to all computing programs? 55-59
  
• Sei-Jong Chung:
  Network protocols: correcting transmission errors of up to two bits. 60-62
  
• Timothy J. Rolfe:
  Program optimization: enforcement of local access and array access via pointers. 63-65
  
• Jonathan P. Bernick:
  A translation of the one-to-one relationship for introductory relational database courses. 66-67
  
• Lisa J. Burnell, John W. Priest, John R. Durrett:
  Assessment of a resource limited process for multidisciplinary projects. 68-71
  
• William S. Curran:
  Teaching software engineering in the computer science curriculum. 72-75
  
• Michael M. Pickard, Jason R. Adams:
  Model determination tool (MDT): a multipurpose software engineering learning utensil. 76-78
  
• Douglas Bell, Mehdi Mir-Ghasemi:
  Teaching data structures using list boxes. 79-81
  
• Charles S. Saxon:
  Object-oriented recursive descent parsing in C#. 82-85
  
• Kenny Hunt:
  Using image processing to teach CS1 and CS2. 86-89
  
• Chenglie Hu:
  A framework for applet animations with controls. 90-93
  
• Russel E. Bruhn, Philip J. Burton:
  An approach to teaching Java using computers. 94-99
  

• Janet Carter, Kirsti Ala-Mutka, Ursula Fuller, Martin Dick, John English, William Fone, Judy Sheard:
  How shall we assess this? 107-123
  
• Thomas L. Naps, Stephen Cooper, Boris Koldehofe, Charles Leska, Guido Rößling, Wanda Dann, Ari Korhonen, Lauri Malmi, Jarmo Rantakokko, Rockford J. Ross, Jay Anderson, Rudolf Fleischer, Marja Kuittinen, Myles F. McNally:
  Evaluating the educational impact of visualization. 124-136
  
• Sylvia Alexander, Martyn Clark, Ken Loose, June Amillo, Mats Daniels, Roger D. Boyle, Cary Laxer, Dermot Shinners-Kennedy:
  Case studies in admissions to and early performance in computer science degrees. 137-147