Large language models (LLMs) are reshaping the educational landscape, particularly in online learning environments where student supervision is often limited. Early evidence and anecdotal reports suggest that the use of AI-generated content is highly prevalent among students. However, definitive statistics remain elusive, primarily due to the challenges associated with distinguishing between AI-generated and human-generated responses. Establishing clear evidence and effective mechanisms for identifying AI-generated responses is crucial for understanding the significance of this challenge and for developing policies to address it. To tackle these issues, we present a large-scale empirical study on the prevalence of AI-generated content in online education. Our study analyzes over 4045 student responses from an introductory MOOC on the Internet of Things, employing textual analysis techniques to evaluate various metrics for identifying AI-generated responses and understanding their characteristics. Our findings reveal that a significant majority of student responses (up to 90.1%) exhibit strong similarities to AI-generated content in both wording and contextual meaning, regardless of the specific LLM or similarity metric employed. In terms LLMs usage, DeepSeek, Gemini, and Grok are the three most popular LLMs used to generate responses.

This work contributes to a better understanding of computing teachers' perceptions of themselves as role models. Role models are described as important to address under-representation, yet there is little in-depth research on how role modeling works and what university teachers in computing can model to broaden participation in the discipline. We will analyze teachers' reflections on how they may, or want to, be perceived by their students, particularly in terms of professional competencies, emotions and attitudes towards well-being. We will use and further develop an already existing framework on role modeling in computing, and we will relate our findings to existing research on computing and science identities. Modeling aspects outside the computing norm can help provide students with a wider notion of what it means to be a computer scientist. Besides developing the theoretical understanding of computing teachers as role models , our work can support various ways of developing computing teachers' competences and departments' teaching culture. The results are one way to contribute to student diversity and equitable access, and more broadly increase the relevance of computing education for sustainability.

This work in progress short paper reports on a pilot study on engineering teachers' observations of students' emotional responses to situations in which students receive feedback on their work. Academic emotions have been a less studied phenomenon compared to the cognitive and social dimensions of teaching and learning in the field of engineering education. However, the interest in academic emotions has been growing during the past decade, as researchers and educators have started to recognize the role emotions play in students' motivation, self-efficacy beliefs and academic success.In this qualitative study we explored engineering educators' experiences on the emotional reactions they have observed in their students at the time students receive feedback on their work. We also asked teachers to elaborate on the emotions they could remember encountering when they were students themselves and received feedback on their work. The preliminary results revealed a variety of emotions from strongly positive to extremely negative emotional responses. We classified each emotion using Pekrun's taxonomy of emotions. As we analyzed the teachers' accounts of student emotions we noted the following aspects: 1) whether the emotion was positive or negative, and 2) whether the emotion was activating or deactivating. The preliminary results showcase both activating and deactivation positive and negative emotions.We concluded by discussing the possible practical implication of our results to engineering teachers. The role of academic emotions in a student's wellbeing and academic success poses a need to recognize the importance of cultivating the art of giving and receiving feedback skills throughout engineering degrees. We discuss what teachers can do when planning a course as well as when providing the feedback to avoid and manage at least some unnecessary detrimental student emotions. The art of receiving feedback - and handling the emotions it might trigger - are part of students' study skills that universities should promote.

Research into the relationship between learning computing and students' attitudes, beliefs, and emotions often builds on theoretical frameworks from the social sciences in order to understand how these factors influence, for example, students' motivation, study practices, and learning results. In this paper we explore the computing education research literature to identify new theoretical constructs that have emerged from this research. We focus on empirical work in programming education that extends or adapts theories or instruments from the social sciences or that independently develops theories specific to programming. From an initial data set of more than 3800 papers published in the years 2010--2019, we identify 50 papers that present a range of domain-specific theoretical constructs addressing emotions, affect, beliefs, attitudes, and self-efficacy. They include 11 validated instruments and a number of statistical models, but also grounded theories and pedagogical models. We summarize the main results of many of these constructs and provide references for all of them. We also investigate how these constructs have informed further research by analysing over 850 papers that cite these 50 papers. We categorize the ways that theories can inform further research, and give examples of papers in each of these categories. Our findings indicate that among these categories, instruments have been most widely used in further research, thus affirming their value in the field.

In order to mature as a research field, computing education research (CER) seeks to build a better theoretical understanding of how students learn computing concepts and processes. Progress in this area depends on the development of computing-specific theories of learning to complement the general theoretical understanding of learning processes. In this paper we analyze the CER literature in three central publication venues -- ICER, ACM Transactions of Computing Education, and Computer Science Education -- over the period 2005--2015. Our findings identify new theoretical constructs of learning computing that have been published, and the research approaches that have been used in formulating these constructs. We identify 65 novel theoretical constructs in areas such as learning/understanding, learning behaviour/strategies, study choice/orientation, and performance/progression/retention. The most common research methods used to devise new constructs include grounded theory, phenomenography, and various statistical models. We further analyze how a number of these constructs, which arose in computing education, have been used in subsequent research, and present several examples to illustrate how theoretical constructs can guide and enrich further research. We discuss the implications for the whole field.

Many universities that have previously focused on offering blended learning or face to face courses are currently starting to offer more and more also online courses. This paper takes a closer look at the background and perceptions of eight teachers who are about to teach an online course for the first time. More specifically, we take a look at the teachers' perceptions of themselves as adopters of new pedagogy and new technology as well as their perceptions of strengths and weaknesses of online courses. The preliminary results suggest that our teachers feel rather comfortable with the new technology and especially with the new teaching methods. Most of the teachers identified themselves as innovators or early adopters of new teaching methods. Teachers perceived flexibility and efficiency as the most prevailing strengths of online teaching. On the other hand, weaknesses included workload, technical challenges, and various topics that relate to the lack of face to face interaction. We conclude by discussing what kind of pedagogical support and training the online teachers would benefit from.

We analyze the Computing Education Research (CER) literature to discover what theories, conceptual models and frameworks recent CER builds on. This gives rise to a broad understanding of the theoretical basis of CER that is useful for researchers working in that area, and has the potential to help CER develop its own identity as an independent field of study.

During the past two years, Massive Open Online Courses (MOOCs) have created wide interest in the academic world raising both enthusiasm for new opportunities for universities and many concerns for the future of university education. The discussion has mainly appeared in non-scientific forums, such as magazine articles, columns and blogs, making it difficult to judge wider opinions within academia. To collect more rigorous data we surveyed teachers, researchers, and academic managers on their opinions and experiences of MOOCs. In this paper, we present our analysis of responses from the computer science academic community (n=137). Their feelings about MOOCs are highly mixed. Content analysis of open-ended questions revealed that the most often mentioned positive aspects included affordances of MOOCs, freedom of time and location for studying, and the possibility to experience teaching from top-level international teachers/experts. The most common negative aspects included concerns about pedagogical designs of MOOCs, assessment practices, and lack of interaction with the teacher. About half the respondents claimed they had not changed their teaching as a result of MOOCs, a small number used MOOCs as learning resources and very few were engaging with MOOCs in any significant way.

Research questions in STEM disciplines are frequently strongly contextualised in the teaching and learning practice of the researcher. In this paper we chart a number of possible paths a researcher can follow from a single research proposition, or fundamental research question, to results which can vary significantly in nature. In order to do this, we establish a theoretical framework for research activity and examine the meaning of “theory” as a cognitive and research tool that helps engineering education practitioners and researchers. The paper reflects on the nature and role of different types of theory at four distinct stages of engineering education research: disciplinary, methodological, analytical, and interpretive. We illustrate how theory applies to the framing and integration of study results, and assists in the process of relating theories of learner development and learning to results of empirical data analysis.

This paper discusses the results of a Grounded Theory study on students experience with introductory programming assignments in the light of social cognitive theory. In previous studies we have found CS majors experienced the process of doing CS1 programming assignments in different ways; but they universally made programming-related self-efficacy assessments along the way. Notably, students may reflect negatively on their self-efficacy after successfully completing an assignment, or positively after struggling with an assignment. CS majors tended to use their comparisons with self and classmates as a base for their self-efficacy perceptions. This paper takes a deeper look at these results from the lens of Bandura's self-efficacy theory with the goal of detailing viable pedagogical interventions to support students' introductory programming course experiences.

In this paper, we introduce a new way to categorise existing educational research making it possible to find new previously overlooked research topics. This novel categorisation system is based on the didactic foci of the research papers. Our categorisation scheme is not data driven as in previously published categorisation systems but is derived from the didactic triangle, which is a theoretical model describing the elements of a teaching-studying-learning processes.

The constant comparative method was used to explicate the experience computing majors have with programming assignments in a CS1 course. Findings from a series of four interviews conducted with a purposeful sample of nine students revealed that the primary reflective experience with programming assignments was emotional. That is, the way students remembered and discussed their experiences with programming assignments was dominated by emotional experiences and reactions. We identified six stages whose dimensions capture the variation in students' emotional experiences with programming assignments. This paper reports the beginnings of analysis geared at developing a theory of students' programming assignment experience, rich in detail and grounded in student experience. When completed such a theory may lead to curricular supports, interventions, or tools, to help steer student experience away from the most harmful of emotional tolls.

Previous reports of a media computation approach to teaching programming have either focused on pre-CS1 courses or courses for non-majors. We report the adoption of a media computation context in a majors' CS1 course at a large, selective R1 institution in the U.S. The main goal was to increase retention of majors, but do so by replacing the traditional CS1 course directly (fully preparing students for the subsequent course). In this paper we provide an experience report for instructors interested in this approach. We compare a traditional CS1 with a media computation CS1 in terms of desired student competencies (analyzed via programming assignments and exams) and find the media computation approach to focus more on problem solving and less on language issues. In comparing student success (analyzed via pass rates and retention rates one year later) we find pass rates to be statistically significantly higher with media computation both for majors and for the class as a whole. We give examples of media computation exam questions and programming assignments and share student and instructor experiences including advice for the new instructor.

The focus of this paper is on science students (CS minors) in a CS1 Java course at a technical university. The following three questions are discussed: 1) Which programming related topics the students at the target CS1 course find difficult to learn, and what is the difference between the students that passed the course and the students that dropped out of the course. 2) What kind of strategies both the passed and the dropout students used when they faced a difficult programming related topic. 3) Why some students decided to drop out of the course.

In this paper we present a phenomenographic analysis of computer science instructors' perceptions of student success. The factors instructors believe influence student success fell into five categories which were related to: 1) the subject being taught, 2) intrinsic characteristics of the student, 3) student background, 4) student attitudes and behaviour and 5) instructor influence on student development. These categories provide insights not only into how instructors perceive students, but also how they perceive their own roles in the learning process. We found significant overlap between these qualitative results, obtained through analysis of semi-structured interviews, and the vast body of quantitative research on factors predicting student success. Studying faculty rather than students provides an alternative way to examine these questions, and using qualitative methods may provide a richer understanding of student success factors.

This study focuses on CS minor students' decisions to drop out from the CS1 course. The high level of drop out percentage has been a problem at Helsinki University of Technology for many years. This course has yearly enrolment of 500-600 students and the drop out percentage has varied from 30-50 percents.Since we did not have clear picture of drop out reasons we conducted a qualitative interview research in which 18 dropouts from the CS1 course were interviewed. The reasons of drop out were categorized and, in addition, each case was investigated individually. This procedure enabled us to both list the reasons and to reveal the cumulative nature of drop out reasons.The results indicate that several reasons affect students' decision to quit the CS1 course. The most frequent reasons were the lack of time and the lack of motivation. However, both of these reasons were in turn affected by factors, such as the perceived difficulty of the course, general difficulties with time managing and planning studies, or the decision to prefer something else. Furthermore, low comfort level and plagiarism played a role in drop out. In addition, drop out reasons cumulated.This study shows that the complexity and large variety of factors involved in students' decision to drop the course. This indicates that simple actions to improve teaching or organization on a CS1 course to reduce drop out may be ineffective. Efficient intervention to the problem apparently requires a combination of many different actions that take into consideration the versatile nature of reasons involved in drop out.