Thought crimes and profanities whilst programming

Where should we draw the line of inappropriate conduct on a course that is given freely to anyone? If an individual starts profusely swearing on a lecture, they are most likely expelled from the class or even from the course. But what if they do it outside the lecture amongst their classmates, amongst a group of anonymous individuals - or by themselves? In this article, we study how students use profanities in source code when they are completing programming assignments on a massive open online course (MOOC). We examine how common it is to curse in source code as well as whether specific assignments incite more cursing than others. Additionally, we investigate differences between participants with regards to cursing. Our results indicate that a considerable amount of participants write curse words whilst programming, but most clean their code for the final submission. The data also shows that there are different degrees of profanity in use, ranging from quite inoffensive words to offensive racial slurs. Finally, we discuss options that one may take when individuals who swear are identified, starting from rescinding their right to study.Peer reviewe

Retention in Introductory Programming

The introductory programming course is one of the very first courses that computer science students encounter. The course is challenging not only because of the content, but also due to the challenges related to finding a place in a new community. Many have little knowledge of what to expect from university studies, some struggle to adjust their study behavior to match the expected pace, and a few simply cannot attend instruction due to e.g. family or work constraints. As a consequence, a considerable number of students end up failing the course, or pass the course with substandard knowledge. This leads to students failing to proceed in their studies at a desirable pace, to students who struggle with the subsequent courses, and to students who completely drop out from their studies. This thesis explores the issue of retention in introductory programming courses through multiple viewpoints. We first analyze how the teaching approaches reported in literature affect introductory programming course pass rates. Then, changes on the retention at the University of Helsinki are studied using two separate approaches. The first approach is the use of a contemporary variant of Cognitive Apprenticeship called the Extreme Apprenticeship method, and the second approach is the use of a massive open online course (MOOC) in programming for recruiting students before they enter their university studies. Furthermore, data from an automatic assessment system implemented for the purposes of this thesis is studied to determine how novices write their first lines of code, and what factors contribute to the feeling of difficulty in learning programming. On average, the teaching approaches described in the literature improve the course pass rates by one third. However, the literature tends to neglect the effect of intervention on the subsequent courses. In both studies at the University of Helsinki, retention improved considerably, and the students on average also fare better in subsequent courses. Finally, the data that has been gathered with the automatic assessment system provides an excellent starting point for future research.Ohjelmointi on nykyajan käsityöläistaito, jolle on akuutti tarve työelämässä. Tämän taidon opettelua harkitseva tietää harvoin, kuinka riippuvainen yhteiskuntamme on ohjelmoinnin tuotoksista eli ohjelmistoista. Ilman ohjelmointia esimerkiksi yhteydenpito, kaupankäynti, matkustaminen ja terveydenhuolto olisivat heikommalla tasolla. Puhelimet eivät toimisi, internettiä ei olisi, eikä lääketeollisuus pystyisi käsittelemään yhtä suuria datamassoja uusia parannuskeinoja etsiessä. Kukaan ei olisi kirjoittanut ohjelmaa, joka auttoi avaruuteen pääsemisessä. Väitöskirjassa tarkastellaan ohjelmoinnin opetusmenetelmiä ja niiden toimivuutta korkeakouluissa sekä esitellään kognitiiviseen oppipoikamalliin (Cognitive Apprenticeship) perustuva “ajatuskäsityöläisten” opetusmenetelmä tehostettu kisällioppiminen (Extreme Apprenticeship). Tehostetussa kisällioppimisessa oppimista edesauttava yksilöllinen ohjaus on mahdollista skaalata satoja opiskelijoita sisältäville kursseille. Väitöskirjatyössä ehdotetaan lisäksi kaikille avoimen verkkokurssin (MOOC) käyttöä yliopisto-opiskelijoiden valintaan sekä tarkastellaan tällaisen valintaväylän toimivuutta tietojenkäsittelytieteen alalla. Väitöskirja käsittelee myös ohjelmointitehtävien automaattista arviointia ja esittelee tähän tarkoitetun Test My Code -järjestelmän, jota voidaan käyttää askeleittaisten ohjeiden ja palautteen antamiseen aloitteleville ohjelmoijille sekä tiedon keräämiseen ohjelmointiprosessissa esiintyvistä ongelmista. Tällaista tietoa voidaan tutkia oppimisanalytiikan menetelmin. Väitöskirjassa tarkastellaan myös aloittelevien ohjelmoijien ensimmäisten ohjelmien kirjoittamisessa esiintyviä ongelmia sekä esitellään ohjelmointitehtävien vaikeuden ennustamiseen sopivia menetelmiä

Transfer-Learning Methods in Programming Course Outcome Prediction

The computing education research literature contains a wide variety of methods that can be used to identify students who are either at risk of failing their studies or who could benefit from additional challenges. Many of these are based on machine-learning models that learn to make predictions based on previously observed data. However, in educational contexts, differences between courses set huge challenges for the generalizability of these methods. For example, traditional machine-learning methods assume identical distribution in all data—in our terms, traditional machine-learning methods assume that all teaching contexts are alike. In practice, data collected from different courses can be very different as a variety of factors may change, including grading, materials, teaching approach, and the students. Transfer-learning methodologies have been created to address this challenge. They relax the strict assumption of identical distribution for training and test data. Some similarity between the contexts is still needed for efficient learning. In this work, we review the concept of transfer learning especially for the purpose of predicting the outcome of an introductory programming course and contrast the results with those from traditional machine-learning methods. The methods are evaluated using data collected in situ from two separate introductory programming courses. We empirically show that transfer-learning methods are able to improve the predictions, especially in cases with limited amount of training data, for example, when making early predictions for a new context. The difference in predictive power is, however, rather subtle, and traditional machine-learning models can be sufficiently accurate assuming the contexts are closely related and the features describing the student activity are carefully chosen to be insensitive to the fine differences.Peer reviewe

Preventing Keystroke Based Identification in Open Data Sets

Large-scale courses such as Massive Online Open Courses (MOOCs) can be a great data source for researchers. Ideally, the data gathered on such courses should be openly available to all researchers. Studies could be easily replicated and novel studies on existing data could be conducted. However, very fine-grained data such as source code snapshots can contain hidden identifiers. For example, distinct typing patterns that identify individuals can be extracted from such data. Hence, simply removing explicit identifiers such as names and student numbers is not sufficient to protect the privacy of the users who have supplied the data. At the same time, removing all keystroke information would decrease the value of the shared data significantly. In this work, we study how keystroke data from a programming context could be modified to prevent keystroke latency based identification whilst still retaining information that can be used to e.g. infer programming experience. We investigate the degree of anonymization required to render identification of students based on their typing patterns unreliable. Then, we study whether the modified keystroke data can still be used to infer the programming experience of the students as a case study of whether the anonymized typing patterns have retained at least some informative value. We show that it is possible to modify data so that keystroke latency based identification is no longer accurate, but the programming experience of the students can still be inferred, i.e. the data still has value to researchers. In a broader context, our results indicate that information and anonymity are not necessarily mutually exclusive.Peer reviewe

Who Continues in a Series of Lifelong Learning Courses?

Publisher Copyright: © 2022 ACM.Although computing education research quite often targets within-university courses, an important role of universities is educating the public through open online lifelong learning offerings. Compared to within-university courses, in lifelong learning, the student population is often more diverse. For example, participants often have more varied motivations and aspirations as well as more varied educational backgrounds. In this work, we explore what kinds of learners attend open online lifelong learning programming courses and what characteristics of learners lead to completing courses and proceeding to subsequent courses. We examine student-related factors collected through surveys in our online course environment. These factors include motivation, previous experience, and demographics. Our results show that motivations, previous experience, and demographics by themselves only explain a small amount of the variance in completing courses or continuing to a subsequent course. At the same time, we identify individual factors that are more likely to lead to learners dropping out (or continuing) in the courses. Our study provides further evidence that lifelong learning benefits most the already educated part of the population with prior knowledge and high motivation. This calls for further studies that seek to identify means to engage and support participants less likely to continue in such courses.Peer reviewe

The State of Disappearing Frameworks in 2023

Disappearing frameworks represent a new type of thinking for web development. In the current mainstream JavaScript frameworks, the focus has been on developer experience at the cost of user experience. Disappearing frameworks shift the focus by aiming to deliver as little, even zero, JavaScript to the client. In this paper, we look at the options available in the ecosystem in mid-2023 and characterize them in terms of functionality and features to provide a state-of-the-art view of the trend. We found that the frameworks rely heavily on compilers, often support progressive enhancement, and most of the time support static output. While solutions like Astro are UI library agnostic, others, such as Marko, are more opinionated.Comment: 15 pages, 1 figure, 2 tables, approved for WEBIST 202

A Study of Keystroke Data in Two Contexts : Written Language and Programming Language Influence Predictability of Learning Outcomes

We study programming process data from two introductory programming courses. Between the course contexts, the programming languages differ, the teaching approaches differ, and the spoken languages differ. In both courses, students' keystroke data -- timestamps and the pressed keys -- are recorded as students work on programming assignments. We study how the keystroke data differs between the contexts, and whether research on predicting course outcomes using keystroke latencies generalizes to other contexts. Our results show that there are differences between the contexts in terms of frequently used keys, which can be partially explained by the differences between the spoken languages and the programming languages. Further, our results suggest that programming process data that can be collected non-intrusive in-situ can be used for predicting course outcomes in multiple contexts. The predictive power, however, varies between contexts possibly because the frequently used keys differ between programming languages and spoken languages. Thus, context-specific fine-tuning of predictive models may be needed.Peer reviewe

Pauses and spacing in learning to program

Conventional wisdom holds that time is an integral part of the learning process. Spacing out learning over multiple study sessions seems to be better for learning than having a single longer study session. Learners should also take pauses from the learning process to absorb, assimilate, and analyze what they have just learned. At the same time, pausing too often can be harmful for learning. Participants of two subsequent introductory programming courses completed programming tasks in an integrated development environment that saved detailed logs of their actions, including time stamps of all the participants' keypresses in said environment. Using this data with background variables and a self-regulation metric questionnaire, we study how the students space out their work, identify trends in between the kinds of pauses the participants took and the course outcomes, and their connection to background variables. Based on our research, students tend to space out their work, working on multiple days each week. In addition, a high relative amount of pauses of only a few seconds correlated positively with exam scores, while a high relative amount of pauses of a few minutes correlated negatively with exam scores. Student pausing behaviors are poorly explained by traditional self-regulation measures such as the Motivated Strategies for Learning Questionnaire and other background variables.Peer reviewe

Supporting Self-Regulated Learning with Visualizations in Online Learning Environments

In this article, we study how visualizations could be used to support students' self-regulation in online learning. We conducted a randomized controlled trial with three groups: one control group without visualization, one treatment group with textual visualization, and one treatment with graphical visualization with information on peers' average achievement. We studied how different visualizations affect students' academic performance and behavior. We focused on four factors; starting, scheduling, earliness and exercise points, where the first three are related to time management and self-regulation. The last factor measures course performance in terms of completed exercises. Our results suggest that the lowest performing students can benefit from a visualization, whereas the highest performing students are not affected by the presence or absence of a visualization. We also found that visualizations that do not provide the means to compare your own performance with others may even be harmful to performance oriented students.Peer reviewe

Tracking Students' Internet Browsing in a Machine Exam

Traditionally, introductory computer science courses have focused on teaching programming, and have not included teaching information retrieval skills. However, a large part of a programmer's time is spent looking at documentation or browsing the internet for guidance on how to solve the small subtasks that programming often consists of or which library to use for a specific need. We have developed a browser-plugin that tracks how students use online resources during a machine exam. Such a tool could be used -- for example -- to detect whether there is a difference between the browsing behavior of high- and low-performing students. To this end, we conduct a case study with the tool where we examine students' browsing in a lab-based programming exam. In the future, the tool could be used to examine students' browsing and possibly inform decisions on how to teach information retrieval skills to students.Peer reviewe