This paper considers Café 2.0, an Automated Feedback system designed to support students' diagrammatic reasoning in STEM disciplines. Café 2.0 relies on a predefined error library, metamodels, and rules to correct students' solutions and deliver formative feedback. Implementing such a system requires a balance between constraining the solution syntax to enable AF and leaving freedom to students to reflect on their solution. This paper aims to evaluate whether the level of freedom provided by our AF system sufficiently prepares students for exams. In the exam, they must reason and construct solutions starting with a blank page. This study is conducted in an introductory programming course (CS1), based on two semesters (in 2022 and 2023), where Café 2.0 supports online homework. Findings reveal a discrepancy between students' performance in online homework and their success on exams. While many students feel comfortable with fill-in-the-blank diagrams in their homework, they struggle with the open-ended nature of exam tasks. Our results show that, among the students who succeeded in their online homework in 2023,$20\%$were still unable to produce any diagram in the exam. Additionally,$70\%$of them could not correctly provide a text description of their solution. To overcome this limitation, this paper proposes an enhanced system that integrates predefined rules with Large Language Models (LLMs). In this framework, LLMs serve as translators. Students can freely create their diagrams and annotate them with their own textual descriptions using a drawing editor. The LLM then maps these representations into a more structured format that aligns with predefined rules. In this way, Café 2.0 can generate accurate feedback. This transformed representation retains the same informational content as the original, differing only in format. This feature will offer students greater flexibility in constructing their solutions while ensuring that feedback remains precise and consistent by limiting the role of LLMs to translation rather than feedback generation.

In computer science, teaching first-year students to approach problems at varying levels of abstraction is both essential and challenging. While abstraction is a key component of problem solving, many students struggle with thinking abstractly. When presented with a problem, students often rush into coding, feeling closer to a solution through immediate feedback from the compiler or by simulating their code. However, this approach can cause them to overlook essential details, as their code may not account for all possible input scenarios. To address this issue, we introduced a programming methodology in our Introduction to Programming (CS1) course. This methodology requires students to first construct a graphical representation of their solution, ensuring coverage of all potential input cases, before translating it into code. To support regular practice in this diagrammatic reasoning process, we developed a learning tool called CAFÉ 2.0. Over the course of the semester, students use CAFÉ 2.0 to solve problems by submitting both a graphical model of their solution and the associated code implementation. In addition to evaluating the final code, CAFÉ 2.0 provides personalized feedback on the graphical model and how well it aligns with the code. This feedback guides students in refining their model and code, and resubmit them to get new feedback. CAFÉ 2.0's unique feature is its ability to automatically generate feedback on graphical models within an interactive online environment, fostering an engaging and supportive learning experience.

Nowadays, employers highly value soft skills, yet many students lack these fundamental abilities. Teaching soft skills involves fostering active student participation and facilitating communication of technical knowledge among peers. This approach presents challenges: (i) creating an engaging learning environment; (ii) ensuring students get timely feedback; (iii) finding an approach that is not too time-consuming for instructors to prepare.

Shaping first-year students' minds to solve problems at different levels of abstraction is both important and challenging. Although abstraction is a crucial skill in problem-solving, especially in STEM subjects, students often struggle with abstract thinking. They tend to focus their efforts on concrete aspects of the problem, where they feel more comfortable and closer to the final solution. Unfortunately, this approach can cause them to overlook critical details related to the problem or its solution. To address this issue in our Introduction to Programming (CS1) course, we introduced a programming methodology that requires students to create a graphical representation of their solution and then derive the code from it. To enable them to practice this diagrammatic reasoning approach on a regular basis, we developed a learning tool called CAFÉ 2.0. It facilitates a semester-long activity in which students solve problems by submitting both a graphical representation of their solution and its implementation. Further to checking the final implementation, CAFÉ 2.0 also provides personalized feedback on how students have graphically modeled their solution and how consistent it is with their code. This paper presents an overview of the features of CAFÉ 2.0 and the methodology it currently supports in the context of our CS1 course. Then, using a survey and learning analytics, this paper evaluates students' interactions with CAFÉ 2.0. Finally, the potential for extending CAFÉ 2.0 to other STEM disciplines is discussed.