Towards the Use of Slice-based Cohesion Metrics with Learning Analytics to Assess Programming Skills

Abstract

In programming education, it makes a difference whether you are dealing with beginners or advanced students. As our future students will become even more tech-savvy, it is necessary to assess programming skills appropriately and quickly to protect them from boredom and optimally support the learning process. In this work, we advocate for the use of slice-based cohesion metrics to assess the process of program construction in a learning analytics setting. We argue that semantically related parts during program construction are an essential part of programming skills. Therefore, we propose using cohesion metrics on the level of variables to identify programmers' trains of thought based on the cohesion of semantically related parts during program construction.

Pinpoint: A Record, Replay, and Extract System to Support Code Comprehension and Reuse

“…To achieve this, Pinpoint creates the slice profile [29] for the dynamic slice of the current selection, i.e., it separates the slice into further sub-slices, each of which contains only code relevant to one attribute. For each attribute, the union slice [21] is created as the union of the backward slices of all statements that use (read) the attribute with the forward slices of the statements that define (write) the attributes within these backward slices. The answer to a "How" question is given by the union slice for the attribute underlying the question, as for example shown in Figure 2.…”