Recent advances in artificial intelligence (AI), including the widespread adoption of foundational models, have triggered changes across Computing Science (CS) programs. Developments include, but are not limited to, revisions to assessment practices, updates to academic integrity policies, curriculum redesign to integrate emerging concepts, and the growing use of conversational agents to support instruction. These developments aim to address effective preparation of graduates for an evolving AI innovation landscape.

Database education is a cornerstone under many of the more popular topics in computer science such as machine learning and visualization. Although, in recent years, more fundamental research into database education has come out, there are many more ways in which it can be extended. Research on the practice of teaching databases, namely on the educational materials and explanations of teachers, can help us create new building blocks for fundamental research. This working group aims to collect and present notional machines of different types, for a wide range of database subtopics. These materials offer and updated context for database educators to design their courses from, as well as open up pathways of further research into database education.

Authentic learning refers to a student's metacognition about what they have learned and not learned. Building students' authentic learning is a primary goal of instructors. Educators also want to be able to assess students' learning. One way to do this is to have a one-on-one conversation with students in which the instructor probes their understanding with a series of questions that require the students to explain why they did what they did, possible alternative approaches, and the implications of their learning. Scaling this type of high-impact 1-1, human-led dialogues with larger classes is a challenge. The present study built a custom generative AI tool and tested its ability to have such a conversation with students. In this paper we report on an experiment which aims to compare students' course performance and attitudes between conditions of instructor-led and AI-led dialogues. We found that regardless of condition, students significantly grew in their self efficacy from the beginning of the semester to the end of each of the two feedback sessions. Additionally, students receiving dialoguing with the AI were significantly less nervous than students dialoguing with the instructor with no differences in other attitude measures such as how deeply they believed they understood their assignments. As such, the intelligent assessor generative AI tool offers the potential to provide scalable feedback to students regardless of class size, likely with little to no detriment to students' growing confidence in their skills.

Conference presentations usually focus on successful innovations: new ideas that yield significant improvements to current practice. Yet educators know that we often learn more from failure than from success. In this panel, we present four case studies of "good ideas" for improving CS education that resulted in failures. Each contributor will describe their "good idea", the failure that resulted, and wider lessons for the CS community.

The model curriculum used to develop, update, and assess IS programs (IS2010) is now nearly a decade old, and an assessment of the curriculum itself indicates that its value is decreasing due to the changing technological and skills demands in the information systems environment. Therefore, the ACM and AIS established an Exploratory Task Force that assessed IS2010 and recommended a taskforce be created to update the content and structure for a new model curriculum. One recurring theme is that current graduates' technical skills do not appear to meet industry needs. The IS discipline must express its core in terms of a standard curriculum to provide a foundation upon which to develop and offer undergraduate IS programs that meet stakeholder demands. A taskforce on the Information Systems Model Curriculum (IS2020) was created following the report and recommendation of the Exploratory Taskforce. This panel seeks to introduce the work of this taskforce as well as engage the IS education community in this effort. Panelists will introduce key components of this process and seek input and feedback. This session should be of interest to all attendees, especially faculty developing college-level curricula in Information Systems.

Relational databases have dominated both industry usage and academic database courses for decades. More recently, there has been a dramatic increase in the use of NoSQL database systems, especially in data science. Bringing NoSQL databases to the classroom is important not only to prepare our students for the technology that they will face, but also because the underlying paradigm introduces new ideas that are not typically emphasized in a relational-only database course alone. However, NoSQL systems seem to receive dramatically less coverage in undergraduate database curricula than relational systems do. This panel discusses examples of how NoSQL can be introduced into undergraduate education, and the possible challenges faced in doing so. Questions from the audience will be invited.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of ten AI assignments from the 2019 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http: //modelai.gettysburg.edu.

Getting alumni effectively engaged as mentors with students is a topic that is of great interest to universities but often hard to do in practice. We have found that with proper structure and support, software project courses provide a good avenue to make these connections between alumni and student and that alumni input can improve the quality of outcomes in these project courses. In this poster the authors describe the alumni mentoring process and how it has evolved over the past three years. In addition, we have begun to collect data from both student teams and alumni to answer three questions: (1) what information are alumni sharing with student teams? (2) is a student team's perception of what was said matching up well with the mentor's perception?, (3) are student teams able to convert alumni feedback into actionable project items?, and (4) at what phase in a software engineering project are alumni mentors most effective? The poster will demonstrate the details of our mentoring process and display our preliminary findings in a colorful fashion convenient to allow others to assess for themselves the potential value of using alumni mentors in software project courses.

At the present, there is a significant lack of programs or resources at the high school level to prepare students for a data driven future. Data Jam is a high school outreach program, that introduces students to data science. The program is organized by members of academia (Carnegie Mellon University, University of Pittsburgh) and industry and research (IBM, Teradata, Pittsburgh Computing Center). Over a period of four months (Oct-Feb of the school year), teachers and students explore the concepts of data science and big data via workshops, exercises, a field trip, and a team project. Data Jam is currently in its fifth year. Participation has grown from an initial pool of seven teams to twenty-five teams last year. Based on teacher and student feedback, we are pleased with the program's success. This poster discusses the goals and structure of Data Jam, its execution, participant feedback, and lessons learned.

In this teaching tip and courseware note we describe a series of hands on activities and exercises that we've used to introduce the notion of big data analytics to a wide range of audience. These exercises range in complexity from a paper and pencil thought exercise, to using Google Trends for simple explorations, to using a spread sheet to simulate the iterative nature of Google's PageRank algorithm, to programming with a Python based map-reduce framework. These exercises have been used in courses to train high school teachers in data science, full semester university courses (undergraduate and graduate), and CS education outreach efforts. Feedback has been positive as to their efficacy.

Puzzle-based learning (PBL) is a new and emerging model of teaching critical thinking and problem solving. Today's market place needs skilled graduates capable of solving real problems of innovation in a changing environment. A learning goal of PBL is to distill domain independent transferable heuristics for tackling problems. While solving puzzles is innately fun, companies such as Google and Yahoo also use puzzles to assess the creative problem solving skills of potential employees. In this interactive workshop we will examine a range of puzzles and games. What general problem solving strategies can we learn from the way we solve these examples? Participants will emerge with the needed pedagogical foundation to offer a full course on PBL or to include it as part of another course.

In this experience report, we describe recent initiatives in global undergraduate Information Systems education at Carnegie Mellon University. The entire systems development core curriculum is now offered at CMU campuses in Pittsburgh and Doha, Qatar. Courses are co-designed and delivered by faculty in both locations with an eye toward consistency of content and assessment, but also with content tuned for local sections. The collaboration and lessons learned among collaborating faculty in two example courses is described.

This paper discusses a learning exercise we use in our beginning programming classes to introduce students to the concepts of abstraction and decomposition. The assignment is to write a perpetual calendar generation program: given a month and a year the program will display the correct monthly calendar. The learning goals of the exercise include how to decompose a large problem into smaller pieces and how to specify what each piece needs to do. This exercise helps students learn the process of incremental and iterative development. More than the actual solution, the value of this exercise is in the several themes of software development that are discussed during its development. We have successfully used this assignment for several years in a variety of CS1/CS2 programming environments (Pascal, C, Java and .net) and also as a Java servlet based web application exercise. Over this period, the case-study has received very favorable feedback from students as to its interestingness and pedagogical value.

No abstract available.

Recursion is a very powerful and useful problem solving strategy. But, along with pointers and dynamic data structures, many beginning programmers consider recursion to be a difficult concept to master. This paper reports on a study of upper-division undergraduate students on their difficulty in comprehending the ideas behind recursion. Three issues emerged as the points of difficulty for the students: (1) insufficient exposure to declarative thinking in a programming context (2) inadequate appreciation of the concept of functional abstraction (3) lack of a proper methodology to express a recursive solution. The paper concludes with a discussion of our approach to teaching recursion, which addresses these issues. Classroom experience indicates this approach effectively aids students' comprehension of recursion.

No abstract available.

No abstract available.