Supervisor: Gabrielle Hansen
Project Description: This project investigates a new introductory IT course, specifically developed for LUR students. The course differs radically from traditional IT intro courses by focusing on mastery-based learning without exams, exercises, or lectures. The goal is to offer a new approach that stimulates independent learning and fosters a sense of accomplishment among students.
Teaching Assistants from Excited: Oda Bang-Olsen, Joakim Pettersen Vassbakk, Elias Borge Svinø

Supervisor: Line Kolas
Project Description: This project focuses on analyzing programming games for adults, aiming to evaluate the suitability of these games for learning programming. The work includes data collection and cleaning to exclude games that do not meet the inclusion criteria. Additionally, researchers and teaching assistants will play and analyze the games using an analytical framework, which is under development. The goal is to understand how these games can serve as learning tools for beginner programmers.
Teaching Assistants from Excited: Anna Holden Jacobsen, Tiril Sjøberg, Håkon Ringen

Supervisors: Aamna Rais Ahmed, Line Kolas
Project Description: This project is a systematic literature review of "Blended Project-Based Learning" (BPBL) in higher computer science education. The goal is to analyze how BPBL is defined, implemented, and evaluated in research. Teaching assistants will be assigned research articles and use an analytical framework to map definitions, implementation methods, and research outcomes related to BPBL. The findings will contribute to understanding BPBL’s impact in computer science education and guide future teaching practices.
Teaching Assistants from Excited: Karan Singh Sandhu, Anton Tveito

Supervisor: Trond Aalberg
Project Description: This project explores the use of ChatGPT as a tool in introductory programming education. Through observations and interviews with students, the researchers will examine how ChatGPT can support learning processes and understanding in programming. The work includes an Excited workday, where researchers and students collaborate to develop an understanding of ChatGPT's potential and limitations as a support tool in teaching.
Teaching Assistants from Excited: Hanna Jacobsen, Anton Tveito, Tiril Sjøberg

Supervisor: Rune Hjelsvold
Project Description: This project, led by Cluster 1 in Excited, analyzes learning outcome descriptions for Norwegian bachelor's programs in IT. The aim is to assess the quality of the descriptions, classify them according to Bloom's taxonomy, and examine the coverage of "soft skills." The project involves downloading and analyzing the latest descriptions, comparing them to previous versions, and performing a consistency check to group similar descriptions across study programs and institutions.
Teaching Assistants from Excited: Birk Strand Bjørnaa, Karan Singh Sandhu, Thomas Nitsche, Cathrine Libæk

Supervisor: Birgit R. Krogstie
Project Description: This project is the first step in a larger initiative aimed at integrating sustainability meaningfully into bachelor’s and master’s theses at IDI. Currently, sustainability is mentioned in learning outcomes to varying degrees, but students often struggle to engage with it in a meaningful way. The goal of this phase is to collect and structure relevant resources that will be made available on a website by the end of March. These resources should help students, supervisors, course coordinators, and examiners incorporate sustainability into their work beyond a superficial requirement. Key tasks include gathering existing frameworks, consulting stakeholders, and compiling examples of sustainability discussions in theses.
Teaching Assistants from Excited: Anton Tveito, Camilla Szwarc Jensen

Supervisors: Guttorm Sindre (NTNU), Mariusz Nowostawski (NTNU), Line Kolås (Nord), Robin Munkvold (Nord)
Project description: This project investigates the differences and similarities in responses from various AI tools over time. Every month, the same five questions—two requiring pure text responses and three involving text and Python—are submitted to ChatGPT, Bing Copilot, and Gemini. The research focuses on how responses vary between tools, users, and over time, as well as whether there is a progression in answer quality. So far, semantic similarity analysis has been conducted using Word documents and a Python script with Sentence-BERT. The next step is a deeper analysis, both manual and automated, starting with the fifth question. Key aspects include examining the topics, required competencies, and suitability of the proposed tasks, as well as analyzing the quality, complexity, and variation in the suggested solutions.