Assessment of student learning achievement in regression tasks
Scatterplots, Regression modelling, Statistical thinking, Vocational education
Many students are under the illusion that the use of IT can accomplish statistical calculations without the need for statistical thinking but the regression models they built are infeasible for making prediction. It is therefore in the present study to assess the operational level of students’ statistical thinking in regression modelling. A sample of students was selected to attempt the seven questions on an individual basis. A qualitative analysis of students’ Responses to each of the questions was performed within the assessment framework of Putt et al. as checking which of the four levels of statistical thinking the students had: idiosyncratic thinking, transitional thinking, quantitative thinking and analytical thinking. The analysis results show that most students attained either quantitative thinking or analytical thinking when handling more technical tasks, but not the tasks of reasoning about data; reasoning about results; and reasoning about conclusions. These reasoning tasks demand statistical communication that should be emphasized and monitored throughout Statistics lessons and written works should be assigned to students so that teachers can provide feedback on their writings, as helping them conceptualize material, make links among concepts, and internalize thinking.
New Ecology for Education: Communication X Learning
Li, W. (2017). Assessment of student learning achievement in regression tasks. New Ecology for Education: Communication X Learning, 81-90. Retrieved from http://repository.vtc.edu.hk/ive-it-sp/97