Informatics in Education

Learning programming has become increasingly popular, with learners from diverse backgrounds and experiences requiring different support. Programming-process analysis helps to identify solver types and needs for assistance. The study examined students’ behavior patterns in programming among beginners and non-beginners to identify solver types, assess midterm exam scores’ differences, and evaluate the types’ persistence. Data from Thonny logs were collected during introductory programming exams in 2022, with sample sizes of 301 and 275. Cluster analysis revealed four solver types: many runs and errors, a large proportion of syntax errors, balance in all features, and a late start with executions. Significant score differences were found in the second midterm exam. The late start of executions characterizes one group with lower performance, and types are impersistent during the first programming course. The findings underscore the importance of teaching debugging early and the need to teach how to program using regular executions.

Multiple choice questions are a convenient and popular means of testing beginning students in programming courses. However, they are qualitatively different from exam questions. This paper reports on a study into which types of multiple choice programming questions discriminate well on a final exam, and how well they predict exam scores.

This paper presents model-based assessment and forecasting of the Lithuanian education system in the period of 2001-2010. In order to obtain satisfactory forecasting results, constructing of models used for these aims should be grounded on some interactive data mining. Data mining of data stored in the system of the Lithuanian teacher's database and of data from other sources representing the state of education system and the demographic changes in Lithuania was used. The models cover the estimation of data quality in the databases, the analysis of flow of teachers and pupils, the clustering of schools, the model of dynamics of pedagogical staff and pupils, and the quality analysis of teachers. The main results of forecasting and integrated analysis of the Lithuanian teachers' database with other data reflecting the state of the education system and demographic changes in Lithuania are presented.