For introductory programming courses, it is crucial to create formative and summarized grading practices that foster growth mindset in students. We explore one way to reimagine and reorient programming assignment grading towards feedback-oriented encouragement for ongoing and continuous learning. This practice of mastery grading encompasses the following key features: (1) students are provided with a comprehensive list of assignment specifications, (2) evaluation of student work is centered on the attainment of specified criteria, employing a nominal scale to denote whether some or all the requirements are met within the deadline, and (3) multiple opportunities are afforded to demonstrate mastery for each specification, without penalties for initial attempts. We share our implementation conducted within an undergraduate first-year web development course, intending it to serve as both a reference and a valuable resource for instructors interested in integrating mastery grading into their own courses.

This study examines metacognition in program comprehension by purposefully inserting errors in a sequence of code tracing and intent problems. Participants were 39 students reasoning about DeMorgan's Laws from a series of test cases. Student performance suggests a range of faulty attributions and a need to further exemplify good and bad practices to develop proper standards for evaluating the products of cognitive processes.

This study examined the effectiveness of sketching flow diagrams in facilitating CS1 students' problem solving and performance on code writing tasks. Five students received training in design strategies and sketched flow diagrams prior to implementing their solution by writing code. Procedures in solving a sequence of four problems depicted in sketches and captured in keystroke log data were coded and scored for each student. The study results showed that specific and generative depictions of procedures predict the correctness of edits made to solutions.

In this panel, we will explore the use of auto-graded programming assignments to support timely and effective feedback to learners via small, scaffolded programming tasks. Program comprehension involves processing at different levels [5,10]. As students proceed through a program, associative processes take place and are described as information as the current statements activate information from the previous statements and from memory of prior knowledge. The most frequently inferred relations by the students are those that provide a coherent understanding of the state changes and outputs of a program [3] as well as the purpose of a piece of code [9]. The resulting, interconnected representation of the program goes beyond the syntax of tokens and statements. The outcome of successful comprehension is a representation that captures the meaning of each statement as students infer the operations of a statement, in terms of the underlying data and control flow, given its function in the context of solving a problem [4]. Although models of program comprehension generally agree regarding the processes by which a student arrives at a mental representation of a program, it is less clear how student-initiated processes play a role in program comprehension, and how they combine with such passive processes to result in comprehension.

Automatically analyzing dialogue can help understand and guide behavior in domains such as counseling, where interactions are largely mediated by conversation.In this paper, we study modeling behavioral codes used to asses a psychotherapy treatment style called Motivational Interviewing (MI), which is effective for addressing substance abuse and related problems.Specifically, we address the problem of providing real-time guidance to therapists with a dialogue observer that (1) categorizes therapist and client MI behavioral codes and, (2) forecasts codes for upcoming utterances to help guide the conversation and potentially alert the therapist.For both tasks, we define neural network models that build upon recent successes in dialogue modeling.Our experiments demonstrate that our models can outperform several baselines for both tasks.We also report the results of a careful analysis that reveals the impact of the various network design tradeoffs for modeling therapy dialogue.Code Count Description Examples Client Behavioral Codes FN 47715 Follow/ Neutral: unrelated to changing or sustaining behavior."You know, I didn't smoke for a while.""I have smoked for forty years now."CT 5099 Utterances about changing unhealthy behavior."I want to stop smoking."ST 4378 Utterances about sustaining unhealthy behavior."I really don't think I smoke too much."Therapist Behavioral Codes FA 17468 Facilitate conversation "Mm Hmm.", "OK.","Tell me more."GI 15271 Give information or feedback."I'm Steve.","Yes, alcohol is a depressant."RES 6246 Simple reflection about the clients most recent utterance.C: "I didn't smoke last week" T: "Cool, you avoided smoking last week."REC 4651 Complex reflection based on a client's history or the broader conversation.C: "I didn't smoke last week."T: "You mean things begin to change".QUC 5218 Closed question "Did you smoke this week?"QUO 4509 Open question "Tell me more about your week."MIA 3869 Other MI adherent,e.g., affirmation, advising with permission, etc. "You've accomplished a difficult task." "Is it OK if I suggested something?"MIN 1019 MI non-adherent, e.g., confrontation, advising without permission, etc. "You hurt the baby's health for cigarettes?" "You ask them not to drink at your house."