Points are widely used design elements in gamified systems. Yet how they motivate is still unclear: what motivational meaning or functional significance do users ascribe to points and when? To answer this question, we conducted a semi-structured interview study with 27 users of two popular gamified platforms, Duolingo and Habitica. Through reflexive thematic analysis, we constructed six different types of functionalisation variously proposed in prior gamification and personal informatics work but often not empirically supported. We highlight the importance of functional design detail (such as points should proportionally reward effort) and derive design guidelines.

This working group is conducting a scoping review exploring how interpretivist research paradigms are articulated, applied, and represented in qualitative research in computer science education. Despite the prevalence of qualitative research in our field slowly growing, interpretivist assumptions remain inconsistently described and understood; the direction of influence between this inconsistency and enduring debates over which paradigms are endorsed in our field remains unclear.

IDEs provide diagnostic tools to help programmers spot mistakes and improve their code. Although these tools are useful for programmers of all levels, they do not capture code patterns that may indicate a misunderstanding of a programming concept rather than a simple typo or oversight. In the early stages of learning to code, misconceptions can be common and difficult to correct without help. PyGuide is an extension for Visual Studio Code that aims to address this gap in IDE diagnostics by highlighting and providing guidance for possible misconceptions in Python code.

Misconceptions about programming concepts can block learners' progress. Automated feedback for misconceptions is appealing because providing feedback while learners work may help them get unstuck faster. However, a key challenge for automating misconception feedback is that the underlying cause of code patterns suggestive of a misconception can vary greatly by learner, meaning that different learners will have different feedback needs. Existing approaches to automated feedback for task-independent misconceptions favour succinct messages that do not overload users with excessive explanation. Although this approach may work well for learners with relatively shallow misconceptions, it also leaves out learners with deeper conceptual issues who arguably have greater need for additional support. We conducted a qualitative study to investigate how learners perceive misconception indicators and how they make sense of feedback. We find that individual learners can view the same issue very differently and face markedly different challenges in making use of feedback. These findings can be used to inform the design of automated feedback that accounts for learners' varying knowledge and skills.

Abstraction is a fundamental skill and concept in computer science and it is also a difficult skill to teach. The purpose of the working group is to analyse different perspectives of abstraction's conceptualisation and ways of teaching the skill. Therefore as a result of the working group we will be first identifying how abstraction is discussed and defined in key literature. As a team we will agree on the perspectives and models we will like to explore in teaching context. Finally we will work with computing educators and computing education researchers to design a pedagogical framework that will enable the development of the abstraction skills.

This poster describes our approach to providing automated feedback for formative exercises in an introductory programming module that uses a creative coding approach. A challenge for automating feedback in creative coding is that automation often necessitates highly constrained programming tasks, which is at odds with the typically open-ended ethos of creative computing. Our approach seeks to strike a balance between providing automatic feedback on fundamental concepts and allowing students to exercise their creativity. Students reported that the automated feedback was useful and motivating, while teaching staff observed that it often provided a starting point for one-on-one help requests from students.

Extensive prior work has identified and described misconceptions held by novice programmers. Much of this prior work has involved at least some automatic detection of potential misconceptions using a variety of methods such as intercepting compiler error messages, pattern matching, and black-box testing. To the best of our knowledge, no independent and flexible tool for automatic detection of misconceptions is currently available to the research community, meaning that detection must be reimplemented from scratch for each new project that aims to understand or support novice programmers using automatic analysis. This is time-consuming work, particularly for misconceptions that require understanding of the context of a program beyond localised syntax patterns. In this paper, we introduce SIDE-lib, a standalone library for detecting symptoms of Python misconceptions. This library is made available with the goal of simplifying and speeding up research on Python misconceptions and the development of tools to support learning. We also describe example use cases for the library, including how we are using it in our ongoing research.

Popular Integrated Development Environments (IDEs) provide features such as syntax highlighting, warnings, and error messages to help programmers find and fix bugs and write cleaner code. Although these tools are available to programmers of all levels, they are designed with the assumption that IDE users know how to code. We present a new project to develop IDE features for novice programmers who may have missing, incomplete, or incorrect understanding of the code constructs they use. We are developing tools for mainstream IDEs that help learners diagnose and recover from misconceptions.

Translation apps and devices are often presented in the context of providing assistance while traveling abroad. However, the spectrum of needs for cross-language communication is much wider. To investigate these needs, we conducted three studies with populations spanning socioeconomic status and geographic regions: (1) United States-based travelers, (2) migrant workers in India, and (3) immigrant populations in the United States. We compare frequent travelers' perception and actual translation needs with those of the two migrant communities. The latter two, with low language proficiency, have the greatest translation needs to navigate their daily lives. However, current mobile translation apps do not meet these needs. Our findings provide new insights on the usage practices and limitations of mobile translation tools. Finally, we propose design implications to help apps better serve these unmet needs.

We present Group Touch, a method for distinguishing among multiple users simultaneously interacting with a tabletop computer using only the touch information supplied by the device. Rather than tracking individual users for the duration of an activity, Group Touch distinguishes users from each other by modeling whether an interaction with the tabletop corresponds to either: (1) a new user, or (2) a change in users currently interacting with the tabletop. This reframing of the challenge as distinguishing users rather than tracking and identifying them allows Group Touch to support multi-user collaboration in real-world settings without custom instrumentation. Specifically, Group Touch examines pairs of touches and uses the difference in orientation, distance, and time between two touches to determine whether the same person performed both touches in the pair. Validated with field data from high-school students in a classroom setting, Group Touch distinguishes among users "in the wild" with a mean accuracy of 92.92% (SD=3.94%). Group Touch can imbue collaborative touch applications in real-world settings with the ability to distinguish among multiple users.

From Harry Potter to American Horror Story, fanfiction is extremely popular among young people. Sites such as Fanfiction.net host millions of stories, with thousands more posted each day. Enthusiasts are sharing their writing and reading stories written by others. Exactly how does a generation known more for videogame expertise than long-form writing become so engaged in reading and writing in these communities? Via a nine-month ethnographic investigation of fanfiction communities that included participant observation, interviews, a thematic analysis of 4,500 reader reviews and an in-depth case study of a discussion group, we found that members of fanfiction communities spontaneously mentor each other in open forums, and that this mentoring builds upon previous interactions in a way that is distinct from traditional forms of mentoring and made possible by the affordances of networked publics. This work extends and develops the theory of distributed mentoring. Our findings illustrate how distributed mentoring supports fanfiction authors as they work to develop their writing skills. We believe distributed mentoring holds potential for supporting learning in a variety of formal and informal learning environments.

Young people worldwide are participating in ever-increasing numbers in online fan communities. Far from mere shallow repositories of pop culture, these sites are accumulating significant evidence that sophisticated informal learning is taking place online in novel and unexpected ways. In order to understand and analyze in more detail how learning might be occurring, we conducted an in-depth nine-month ethnographic investigation of online fanfiction communities, including participant observation and fanfiction author interviews. Our observations led to the development of a theory we term distributed mentoring, which we present in detail in this paper. Distributed mentoring exemplifies one instance of how networked technology affords new extensions of behaviors that were previously bounded by time and space. Distributed mentoring holds potential for application beyond the spontaneous mentoring observed in this investigation and may help students receive diverse, thoughtful feedback in formal learning environments as well.

Interaction logs generated by educational software can provide valuable insights into the collaborative learning process and identify opportunities for technology to provide adaptive assistance. Modeling collaborative learning processes at tabletop computers is challenging, as the computer is only able to log a portion of the collaboration, namely the touch events on the table. Our previous lab study with adults showed that patterns in a group's touch interactions with a tabletop computer can reveal the quality of aspects of their collaborative process. We extend this understanding of the relationship between touch interactions and the collaborative process to adolescent learners in a field setting and demonstrate that the touch patterns reflect the quality of collaboration more broadly than previously thought, with accuracies up to 84.2%. We also present an approach to using the touch patterns to model the quality of collaboration in real-time.

We present the Input Observer, a tool that can run quietly in the background of users' computers and measure their text entry and mouse pointing performance from everyday use. In lab studies, participants are presented with prescribed tasks, enabling easy identification of speeds and errors. In everyday use, no such prescriptions exist. We devised novel algorithms to segment text entry and mouse pointing input streams into "trials". We are the first to measure errors for unprescribed text entry and mouse pointing. To measure errors, we utilize web search engines, adaptive offline dictionaries, an Automation API, and crowdsourcing. Capturing errors allows us to employ Crossman's (1957) speed-accuracy normalization when calculating Fitts' law throughputs. To validate the Input Observer, we compared its measures from 12 participants over a week of computer use to the same participants' results from a lab study. Overall, in the lab and field, average text entry speeds were 74.47 WPM and 80.59 WPM, respectively. Average uncorrected error rates were near zero, at 0.12% and 0.28%. For mouse pointing, average movement times were 971 ms and 870 ms. Average pointing error rates were 4.42% and 4.66%. Average throughputs were 3.48 bits/s and 3.45 bits/s. Device makers, researchers, and assistive technology specialists may benefit from measures of everyday use.