This innovative practice full paper describes the author's successful approach in introducing the concepts of big data to a wide range of students at various levels. The term big data has been around since the 1990s. In the last fifteen years, it has been a staple component of undergraduate and graduate curricula-either as a course of its own or as a part of a course on data science, machine learning etc. The approach described in this paper for motivating and explaining the big data concepts has been used in graduate, undergraduate, nonmajor, and high-school classes. Judging from student feedback, these pedagogical strategies have been well received. This paper highlights the author's experience in not just what to teach but also how to teach it.

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.

Service-learning presents a unique opportunity to integrate real world experience into information systems (IS) curricula. Problem scoping is the process by which project members define the problem that they need to solve. It is typically not included in IS service-learning courses. We posit that with increasingly complex and ill-defined problems that face IS graduates, acquiring problem scoping skills is essential to help students work with emerging problems in today’s workplace. We ask the following question: in what ways can students learn problem scoping skills in service-learning IS courses? This paper presents findings from a research-driven pedagogy study consisting of pre- and post- scenarios that students considered at the beginning and end of the semester in a service-learning undergraduate IS course. Through a qualitative analysis of students’ responses about the first two steps that they would take in working on the hypothetical projects, we identified thirteen categories and three themes. The emerging themes provide evidence of students' increased focus on problem scoping and context, on the relationship with the client, and on moving away from immediately delving into technical problem solving. Our findings provide significant contributions to the IS literature on service-learning as well as transferable course design principles.

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 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.

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 Association of Computing Machinery (ACM) and the Association for Information Systems (AIS), two global professional and academic societies with a stake in Information Systems (IS) education, are engaged in a project to revise the Information Systems Curriculum for bachelor's degrees. A joint taskforce on the Information Systems (IS2020) Model Curriculum was created following the report and recommendation of an Exploratory Taskforce. This panel seeks to introduce the work of this taskforce as well as engage the AMCIS community in this effort. The taskforce seeks to facilitate broad feedback during the IS2020 development process through surveys and open feedback requests. 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.

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.

Information Systems (IS) pedagogy research supports the use of collaborative learning strategies that are based on the belief that learning increases when students work together to solve problems and develop cooperative learning skills. The use of innovative active learning approaches instead of lecture-based approaches have helped to engage student learning and build a broader range of skills and experiences (e.g., [1, 2]). In this project, we present an empirical comparison of two active learning classroom approaches - the speed dating method and a traditional presentation format. The speed dating method supports low-cost rapid comparison of project ideas, design, application and progress in a structured and bounded series of serial engagements. In contrast, traditional student presentations allow individuals to provide content but offer somewhat limited interactions. We analyzed data from 174 student surveys and in-class researcher observations of student engagement in an undergraduate senior capstone course entitled, Innovation in Information Systems. The course is centered on studio-based learning as assignments are primarily project-based, students' work is periodically evaluated through critiques, and students continuously engage in critiquing peers' work [3]. The course utilized an alternating series of speed dating and presentation session formats. Our analysis resulted in three main findings. First, students reported receiving and giving much more helpful feedback during the speed dating sessions than in the presentation sessions. Second, students reported being significantly more engaged during the speed dating sessions than in the presentation sessions Finally, classroom observations of engagement showed that students were significantly more engaged in the speed dating session as compared to the presentation session [4]. We believe these findings demonstrate that the speed dating method is a more effective alternative to a presentation format and is a useful complement to other collaborative learning methodologies.

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.

Bridges is the current main system at the Pittsburgh Supercomputing Center. Given the complexity of the system and the volume of its use, it is a very good environment for exploring the potential of machine learning techniques in studying sub-optimal performance. This short report discusses preliminary and ongoing work of a new graduate student exploring this novel realm. Our initial focus has been on learning to predict the occurrence of NFS time out errors from preceding syslog messages.

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.

This book provides insights drawn from the authors extensive experience in teaching Puzzle-based Learning. Practical advice is provided for teachers and lecturers evaluating a range of different formats for varying class sizes. Features: suggests numerous entertaining puzzles designed to motivate students to think about framing and solving unstructured problems; discusses models for student engagement, setting up puzzle clubs, hosting a puzzle competition, and warm-up activities; presents an overview of effective teaching approaches used in Puzzle-based Learning, covering a variety of class activities, assignment settings and assessment strategies; examines the issues involved in framing a problem and reviews a range of problem-solving strategies; contains tips for teachers and notes on common student pitfalls throughout the text; provides a collection of puzzle sets for use during a Puzzle-based Learning event, including puzzles that require probabilistic reasoning, and logic and geometry puzzles.

One of the challenges in implementing a Puzzle-based Learning approach is taking a love of puzzles, or a desire to make students think in a more open-ended fashion, and making it work in a classroom environment. Many courses reward students for sitting quietly and, when prompted, answering a set of well-defined questions with rehearsed answers built from what their teacher has said. When we describe an effective teaching approach to support Puzzle-based Learning, we are not just talking about buying a book or finding some problems, we are talking about a complete change in the way that many of us think about working with our students.

Computing systems may record facts about users such as their click behaviors, their accuracies, and their rates of recurrence, and may then act on predictions of their behaviors and preferences. However, sequences of key presses and mouse movements do not capture their immediate desires and needs during a task. Affect-mediated systems, computing systems that adapt to the emotional state of users, can better respond to these momentary shifts in demand. This paper presents a study of how affect mediation can help improve a user's task performance. Designed as a children's game, our experimental system uses facial expressions to regress the user to an earlier, easier game phase according to perceived unease. Through experimentation with child participants, we found that children performed better with the affect-sensitive version of the game than with the non-affect-sensitive version. We hope these results will support the future design of affect-sensitive machines that can help users complete tasks.