Allen B. Tucker

A model curriculum for a liberal arts degree in computer science

This report proposes developing a rigorous undergraduate curriculum for a B.A.-degree program in computer science. The curriculum is intended as a model not only for high-quality undergraduate colleges and universities, but also for larger universities with strong computer science programs in a liberal arts setting.

Strategic directions in computer science education

Public support for universities and thetraditional balance between researchand teaching have each come under in-creased scrutiny and criticism. At thesame time, modern society grows evermore dependent on computing technol-ogy and many sectors of science andindustry anticipate ongoing shortages ofwell trained computer scientists and en-gineers.The distance between the foundationsof computing and its research and appli-cation frontiers is considerably shorterthan in many other fields. As a result,the curriculum in computer science andengineering (CS&E) faces constant evo-lutionary pressure to integrate new crit-ical developments. The rapid changes intechnology, however, also affect the pro-cess of educational delivery. Recent ad-vances in networking and graphics, forexample, make it possible to developeffective pedagogical tools and sharethem among many different educationalinstitutions.CS&E educators must play a key rolein responding to these changes. To re-main effective, CS&E education mustevolve in both method and content asour discipline progresses. Educatorsneed to champion new technologies thatincrease the quality of education, pro-vide opportunities to a wider class ofstudents, and prepare graduates to par-ticipate in the development of technolo-gies that ensure the safety, privacy, em-powerment, and competencies of futurecitizens. Meeting this challenge will re-quire fundamental changes in the wayin which faculties represent the princi-ples and practice of CS&E at all educa-tional levels.

Revitalizing computing education through free and open source software for humanity

The humanitarian focus of socially useful projects promises to motivate community-minded undergrads in and out of CS.

Our curriculum has become math-phobic!

The paper [2] argued that mathematical ideas play an important role in the computer science curriculum, and that Discrete Mathematics needs to be taught early in the computer science curriculum. In this follow-up paper, we present evidence that computer science curricula are drifting away from a fundamental commitment to theoretical and mathematical ideas. We propose some actions that can be taken to help reverse this drift.

Flexible design: a summary of Computing Curricula 1991

A summary is given of Computing Curricula 1991, which provides curricular guidance for implementing undergraduate programs to faculties of all institutions that offer concentration in computing. Program goals and graduate profiles are discussed. Underlying principles for curriculum design are examined. The implementation of these principles is considered.<<ETX>>

A perspective on machine translation: theory and practice

The idea of programming a computer to translate text from one language to another is not a new one. Experiments in the 1950s seemed to herald great promise, but model machine-translation systems of the early 1960s failed to live up to the advanced billing. The strongest criticism of these early efforts came from the Automatic Language Processing Advisory Committee in 1966 [1], which concluded that fully automatic high-quality machine translation was neither possible at that time nor foreseeable. Research continued and substantial advances were made. Notable among them is the Russian-English translation system developed at Georgetown University [21]. Still in use at Oak Ridge National Laboratory, the Georgetown system translates nuclear physics texts from Russian into rough-but-readable English that is comprehensible to the subject-matter expert, in this case, the physicist. SYSTRAN [5], a more recent derivative of the Georgetown system, has been in productive use since the mid 1970s, most notably at Wright-Patterson Airforce Base in Ohio for translating Russian to English and at the European Economic Community (EEC) headquarters in Luxembourg for translating English to French, French to English, and English to Italian. A more recent Georgetown derivative, installed at the Pan

New directions in the introductory computer science curriculum

Our goals are to review the evolution of curriculum developmen~ examine alternative curricular approaches, and explore new trends in the design of introductory computer science courses. We review the historical evolution of introductory computer science curricula, including Curriculum 68 and 78, the Liberrd Arts Model Curriculum of 1986, the Deming committee’s comprehensive (broad) approach in 1989, and the implementation of this approach in the recent report Computing Curricula 1991. Alternative approaches to introductory curriculum development are presented in terms of dichotomies such as depth versus breadth, single vemus multiple paradigm, conceptual versus pmctical, and active versus passive learning. The role of programming, mathematics, laboratories, and visualization in the curriculum is examined, We then focus more specifically on the goals and scope of specitlc approaches to the introductory courses, examining the role of introductory computer science within the technical and social context of the discipline as a whole.

Using open source software to engage students in computer science education

This panel will discuss issues and methods for incorporating free and open source software (FOSS) in computer science education. The panelists are investigating approaches to student participation in FOSS that produce results that are contributed to the FOSS community and actually used by others.

Has our curriculum become math-phobic? (an American perspective)

We are concerned about a view in undergraduate computer science education, especially in the early courses, that its okay to be math-phobic and still prepare oneself to become a computer scientist. Our view is the contrary: that any serious study of computer science requires students to achieve mathematical maturity (especially in discrete mathematics) early in their undergraduate studies, thus becoming well-prepared to integrate mathematical ideas, notations, and methodologies throughout their study of computer science. A major curricular implication of this theme is that the prerequisite expectations and conceptual level of the first discrete mathematics course should be the same as it is for the first calculus course --- secondary school pre-calculus and trigonometry. Ultimately, calculus, linear algebra, and statistics are also essential for computer science majors, but none should occur earlier than discrete mathematics. This paper explains our concerns and outlines our response as a series of examples and recommendations for future action.

Computing as a discipline: preliminary report of the ACM task force on the core of computer science

It is ACMs 40th year and an old debate continues. Is computer science a science? An engineering discipline? Or merely a technology, an inventor and purveyor of computing commodities? What is the intellectual substance of the discipline? Is it lasting, or will it fade within a generation? Do core curricula in computer science and engineering accurately reflect the field? How can theory and lab work be integrated in a computing curriculum? We project an image of a technology-oriented discipline whose fundamentals are in mathematics and engineering — for example, we represent algorithms as the most basic objects of concern and programming and hardware design as the primary activities. The view that “computer science equals programming” is especially strong in our curricula: the introductory course is programming, the technology is in our core courses, and the science is in our electives. This view blocks progress in reorganizing the curriculum and turns away the best students, who want a greater challenge. It denies a coherent approach to making experimental and theoretical computer science integral and harmonious parts of a curriculum. Those in the discipline know that computer science encompasses far more than programming. The emphasis on programming arises from our long-standing belief that programming languages are excellent vehicles for gaining access to the rest of the field — but this belief limits out ability to speak about the discipline in terms that reveal its full breadth and richness. The field has matured enough that it is now possible to describe its intellectual substance in a new and compelling way. In the spring of 1986, ACM President Adele Goldberg and ACM Education Board Chairman Robert Aiken appointed this task force with the enthusiastic cooperation of the IEEE Computer Society. At the same time, the Computer Society formed a task force on computing laboratories with the enthusiastic cooperation of the ACM. The charter of the task force has three components:Present a description of computer science that emphasizes fundamental questions and significant accomplishments. Propose a new teaching paradigm for computer science that conforms to traditional scientific standards and harmoniously integrates theory and experimentation. Give at least one detailed example of a three-semester introductory course sequence in computer science based on the curriculum model and the disciplinary description. We immediately extended our task to encompass computer science and computer engineering, for we came to the conclusion that in the core material there is no fundamental difference between the two fields. We use the phrase “discipline of computing” to embrace all of computer science and engineering. The rest of this paper is a summary of the recommendation. The description of the discipline is presented in a series of passes, starting from a short definition and culminating with a matrix as shown in the figure. The short definition: Computer science and engineering is the systematic study of algorithmic processes that describe and transform information: their theory, analysis, design, efficiency, implementation, and application. The fundamental question underlying all of computing is, “What can be (efficiently) automated?” The detailed description of the field fills in each of the 27 cells in the matrix with significant issues and accomplishments. (That description occupies about 16 pages of the report.) For the curriculum model, we recommend that the introductory course consist of regular lectures and a closely coordinated weekly laboratory. The lectures emphasize fundamentals; the laboratories emphasize technology and know-how. The pattern of closely coordinated lectures and labs can be repeated where appropriate in other courses. The recommended model is traditional in the physical sciences and in engineering: lectures emphasize enduring principles and concepts while laboratories emphasize the transient material and skills relating to the current technology.