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ABSTRACT

Informatics education, not only in higher but also in secondary education, is often assisted by special learning software to teach the fundamental ideas of algorithms [2]. In this context pupils also learn the basics of programming using didactically reduced, textbased or visual programming languages. Therefore in Germany, in some federal countries (for example Bavaria), where the basics of algorithms are already taught in the 7^th grade (age 12 to 13 years), age-based learning and programming environments, such as Karel, the robot and Kara, the programmable ladybug [1], are used. Although the design of these environments is age-based, working with them to solve algorithmic problems often causes problems in the classroom. These tools give feedback to the learners based on the analysis of a current solution attempt without taking the previous problem solving process into account. The system messages are often rather technical and therefore hardly helpful especially for weaker learners to enable them to correct arisen problems by themselves. In order to give optimal support to pupils in these situations and therefore improve the learning processes, the learner-system interaction of the used educational software environments should be enhanced and better be adapted to the learners? individual problem solving strategies.

The main objective of this research project is to find out, to what extent the automated diagnosis of a problem solving strategy of a learner is possible, and to what extent this knowledge can be used to enhance the learner-system interaction. Starting from the advantages and disadvantages of standardized process observation methods, two software-based research instruments for the system supported diagnosis of the individual proceedings, using the learning environment Kara, were designed and implemented. With the first component learner-system interactions are recorded, the second one provides functions to analyse the collected data. Using test-cases gives a first idea of the quality of the solution attempts.

The requirements for the software components resulted from several test scenarios with a small number of participants with different qualification in computer science (from novices to graduating computer science students). During these tests each individual was observed by a researcher and additionally interviewed afterwards. A first version of the implemented instruments was tested in case studies with more than 100 participants (12 to 13 years old) from Bavarian grammar schools to evaluate the suitability for daily use. During the studies the learners were asked to solve three given tasks in a session of 45 minutes, provided by the Kara system, individually (one pupil per computer), but communication between the test persons was allowed. The tasks required knowledge of the control structures (sequence, selection, iteration).

The results of these studies indicate that it is possible to identify and to evaluate different problem solving patterns with the help of the developed instruments. To identify different types of learners? strategies it is necessary to combine the various kinds of visualizations of the collected data. To support automatic categorization pattern-recognition methods will be used. The collected ordinal (test-case results) and nominal data can be used for analyses of the correlation between different factors (for example number of error messages or program executions compared with the assessment of the solution attempt) with methods of descriptive statistics.

REFERENCES

• 1: Reichert, R. 2003. Theory of Computation as a Vehicle for Teaching Fundamental Concepts of Computer Science. Doctoral Thesis. No. 15035. ETH Zurich.
• 2: Andreas Schwill, Computer science education based on fundamental ideas, Proceedings of the IFIP TC3 WG3.1/3.5 joint working conference on Information technology : supporting change through teacher education: supporting change through teacher education, p.285-291, January 1997, Kiryat Anavim, Israel

INDEX TERMS

Primary Classification:
  K. Computing Milieux
  K.3 COMPUTERS AND EDUCATION
      K.3.2 Computer and Information Science Education
          Subjects: Computer science education

Additional Classification:
  K. Computing Milieux
  K.3 COMPUTERS AND EDUCATION
      K.3.2 Computer and Information Science Education
          Subjects: Curriculum; Self-assessment

General Terms:
Algorithms, Human Factors, Languages, Measurement, Performance

Keywords:
algorithms, didactics of informatics, kara, problem solving process, secondary computer science education, tool-based analysis