Raymond S. Pettit teaches courses in programming, artificial intelligence, objected oriented design, algorithms, theory of computation, and related subjects in ACU's School of Information Technology and Computing. Prior to joining the ACU faculty, he spent twenty years in software development, research, and training the Air Force Research Lab and NASA's Langley Research Center as well as private industry. His current research focuses on how automated assessment tools interact with student learning in university programming courses. Are Automated Assessment Tools Helpful in ProgrammingCourses? AbstractAutomated assessment tools (AATs) are growing in popularity in introductory programming courses, but researchers may have a difficult time synthesizing valid data to draw conclusions about the tools' usefulness. Our first step addressing this issue was to break down our overriding question-are automated assessment tools helpful in programming courses?-into four more specific questions: (1) Have AATs proven to be helpful in improving student learning? (2) Do students think that AATs have improved their performance? (3) After having used the tools, do instructors think that the tools have improved their teaching experiences? and (4) Is the assessment performed by AATs accurate enough to be helpful? In discussing the many AATs that exist, many researchers have only reported results relevant to one or two of these specific questions. We address each of our four questions separately and draw on data from 24 different tools to arrive at our conclusions. We determine that the literature demonstrates AATs helpfulness in student learning, instructor support, and assessment accuracy. However, we found results about students' opinions regarding the helpfulness of AATs to be inconclusive. Given our findings, we make suggestions both for instructors using these tools and to researchers creating them.

As automated tools for grading programming assignments become more widely used, it is imperative that we better understand how students are utilizing them. Other researchers have provided helpful data on the role automated assessment tools (AATs) have played in the classroom. In order to investigate improved practices in using AATs for student learning, we sought to better understand how students iteratively modify their programs toward a solution by analyzing more than 45,000 student submissions over 7 semesters in an introductory (CS1) programming course. The resulting metrics allowed us to study what steps students took toward solutions for programming assignments. This paper considers the incremental changes students make and the correlating score between sequential submissions, measured by metrics including source lines of code, cyclomatic (McCabe) complexity, state space, and the 6 Halstead measures of complexity of the program. We demonstrate the value of throttling and show that generating software metrics for analysis can serve to help instructors better guide student learning.

No abstract

No abstract

No abstract