Eno Tõnisson

Python prevails

In this article we describe why we changed the language of our introductory programming course for first year math and computer science students from Java to Python. We explain why we chose Python over the other languages, how we built up the course and how we ran it as an experiment with a small group (about 25% of students). The experiment itself lead us to decide to use Python with all the students starting the next, 2009/2010 study year.

What's in a Name?: International Interpretations of Computing Education Terminology

The ACM, the AIS, and the IEEE Computer Society have jointly defined five computing disciplines: computer engineering, computer science, information systems, information technology, and software engineering. These terms and many others are used as the names of educational programmes. Across the world, the same name may be used for quite different programmes and different names for similar programmes. This makes it difficult for potential students, employers, and educators to determine the nature of a particular programme and how it compares to others. Because computing is global, greater clarity in terminology is required. We have surveyed academics and literature internationally to determine the scale of the nomenclature issue across the globe. A consistent naming scheme would be ideal, but the different terminologies are now entrenched in different countries, so this paper provides the next best option, a taxonomy of the principal terms and their meanings.

Automatic Assessment of Programming Assignments Using Image Recognition

Automatic assessment of programming tasks in MOOCs (Massive Open Online Courses) is essential due to the large number of submissions. However, this often limits the scope of the assignments since task requirements must be strict for the solutions to be automatically gradable, reducing the opportunity for solutions to be creative. In order to alleviate this problem, we introduce a system capable of assessing the graphical output of a solution program using image recognition. This idea is applied to introductory computer graphics programming tasks whose solutions are programs that produce images of a given object on the screen. The image produced by the solution program is analysed using image recognition, resulting in a probability of a given object appearing in the image. The solution is accepted or rejected based on this score. The system was tested in a MOOC on 2,272 solution submissions. The results contained 4.6% cases of false negative and 0.5% cases of false positive grades. The method introduced in this paper saved approximately one minute per submission of the instructors’ time compared to manual grading. A participant survey revealed that the system was perceived to be functioning well or very well by 82.1% of the respondents, with an average rating of 4.4 out of 5.

Self- and Automated Assessment in Programming MOOCs

This paper addresses two MOOCs in Estonian about programming where different kinds of assessment were used. We have used two kinds of automated assessment: quizzes in Moodle and programming exercises with automated feedback provided by Moodle plug-in VPL. We also used two kinds of self-assessment: (1) self-assessment questions with feedback and explanations for every answer and (2) so-called “troubleshooters” for every programming exercise, which contain answers to the questions that can arise during the solution of a given exercise. This paper describes our experience in the creation of quizzes, programming exercises, and tests for automated feedback, self-assessment questions, and troubleshooters. The paper discusses the problems and questions that arose during this process and presents learners’ opinions about self- and automated assessment. The paper concludes with a discussion of the impact of self- and automated assessment in MOOCs, describes the work of MOOC organizers and the behaviour of learners in MOOCs.

Students' comparison of their trigonometric answers with the answers of a computer algebra system

Comparison of answers offered by a computer algebra system (CAS) with answers derived by a student without a CAS is relevant, for instance, in the context of computer-aided assessment (CAA). The issues of identity, equivalence and correctness emerge in different ways and are important for CAA. These issues are also interesting if a student is charged with the task of comparing the answers. What will happen when students themselves are encouraged to analyse differences, equivalence and correctness of their own answers and CAS answers? What differences do they notice foremost? Would they recognise equivalence/non-equivalence? How do they explain equivalence/non-equivalence? The paper discusses these questions on the basis of lessons where the students solved trigonometric equations. Ten equations were chosen with the aim to ensure that the expected school answer and the CAS answer would differ in various ways. Three of them are discussed more thoroughly in this paper.

Automatic Translation of Computer Algebra Systems' Worksheets

There can be varying degrees of difference between the commands, syntax, etc., of computer algebra systems. Sometimes, translation from one computer algebra system to another is needed. As the languages of computer algebra systems are similar to programming languages, the translation techniques used in case of programming language seem to be productive. This paper focuses on syntax-directed translation where grammars have a central role. The area of commands is restricted to the commands useful for school mathematics. A prototype is developed for translating the worksheets of Maple, Maxima and WIRIS.