Gamification, the use of game elements in non-game contexts, is already widely used to enhance learner engagement and motivation. Gamification motivates by adding gamefulness to the learning tasks. It is largely agreed that the success of applying gamification to educational contexts strongly depends on the way the gamification is designed & implemented. The goal of this BOF will be to provide a platform for discussing and brainstorming of different ways in which Computer Science courses could be gamified. BOF participants will form groups which will be tasked with developing scenarios of gamification design for a CS course. The groups will be led by the BOF organizers & other participating colleagues who are experienced in applying gamification in their courses. The guiding questions will include: What should be the steps in the gamification design process, where do we start? What are the goals? Which learning activities are suited for gamifying? Which game elements - the building blocks that are used for creating gameful experiences - could be used in the selected contexts? What gamification platforms or tools can be used to implement the designed gamification? At the end, the groups will be invited to present their gamification scenarios. We hope that in this BOF we will collectively identify new and interesting strategies for creating successful and sustainable gameful experiences in CS courses which will contribute to learners' engagement & motivation.

Effective gamification can only be based on understanding the relationship between learner motivation and the game elements which are used to gamify learning activities. Although frequently mentioned, Virtual Currency (VC) remains underused and scarcely studied in educational gamification. As a motivational affordance, VC can be thought of as supporting different types of motivation, but currently, there is a lack of empirical studies which investigate this. Recognizing this gap, the purpose of our study was to empirically investigate whether and how gamifying learning activities with virtual currency can engender motivation for out-of-class practicing and what type of motivation. In the limited research others have conducted, VC has been studied largely in combination with other game elements, which does not allow reaching reliable conclusions about the impact of the individual elements. For this reason, we studied the effects of VC in a gamified Discrete Math course isolated from other game elements. The study showed that using VC to gamify practicing increased students’ practicing activity, which resulted in improved academic performance. The study also revealed that while gamified practicing did not increase students’ intrinsic motivation, it supported internalization of motivation towards this learning activity.

This Full Paper in the Research-to-Practice track builds on research in gamification of instruction. A primary objective of gamifying learning is to encourage and sustain students' engagement in activities by making them more gamelike. However, it is still unclear what structures borrowed from games could afford such a “game-like” experience and in what contexts. Embedding classmates' duels in learning settings seems a promising gamification strategy for exploration due to its potential of increasing motivation and engagement through a “game-like” experience. Similar effects are expected from using virtual currency, another design element popular in video games. Accordingly, the goal of this study is to empirically investigate whether the incorporation of dueling leads to increased interest in out-of-class practicing. In addition, we were interested in investigating to what an extent adding virtual currency can boost students' interest in dueling. An introductory class in Python programming served as the experimental environment. Unlike most studies in which the control group works in a non-gamified condition and the experimental group works in a gamified condition, this experiment uses a different approach. We investigated the impact on learners' engagement of adding duels to an ongoing gamified activity. Engagement indicators were measured and logged throughout the semester while student motivation was examined through surveys. The opportunity to challenge classmates appeared after the first exam (around one-third of the semester) and continued for the remaining two thirds of the class time. Thus, the same students in the new condition served as the experimental group. After the third exam, we added virtual currency. Students were able to earn virtual currency by issuing and responding to duel requests as well as by doing extra practice. This had a noticeable effect on the use of dueling. The empirical results of the study show that overall, for this group of students, adding duels alone had no positive effect on students' engagement with the gamified practicing system. However, evidence from the survey suggests that the studied combination of gamification elements (points, badges, leaderboard, avatars, duels, and virtual currency) did increase student intrinsic motivation.

There has been an increasing effort to make activities that are not inherently interesting for all learners more attractive through gamification. Although the research on the effectiveness of educational gamification has been growing, the effects of some gamification elements on learners' motivation and engagement are not well understood. In response to this gap, in this paper we describe an experience of gamifying out-of-class practicing with Virtual Currency (VC) in a Computer Networking course. The results of our study show that the VC-based gamification (without interactions with any other gamification elements) had a positive impact on student engagement compared to non-gamified online practicing. The learners' VC earning and spending behaviors indicate also that Virtual Currency brings additional objectives for students to strive for, besides grades.

The low performance and drop-outs in Computer Science classes are frequently attributed to lack of student engagement and motivation. Meanwhile, gamification increasingly attracts the interest of educators due to its potential to foster motivation and behavioral changes in learning contexts. This virtual workshop introduces participants to the concept of gamification and the use of OneUp Learning, a platform aimed at gamifying academic courses. OneUp is aimed at encouraging students' out-of-class practicing and increasing their overall engagement in the course through employing game design elements. The platform is course-independent and allows the instructor to turn on and off a number of gamification features, including avatars, points, leaderboards, progress bar, content unlocking, goal-setting, badges, virtual currency, duels, etc. For using OneUp, instructors have to provide practice exercises. Apart from the standard question types, OneUp supports Parson's and programming problems, as well as Flash cards. The workshop offers an interactive tutorial and individual work. The tutorial will introduce the OneUp platform and will demonstrate its use for gamifying a course. Workshop attendees will be engaged in hands-on experience. An OneUp course shell will be created and populated with sample data for each workshop attendee. After the tutorial, the participants will be able to explore and further gamify their own OneUp demo course. We will also demonstrate Quest, a repository for sharing OneUp problems.

The low performance and drop-outs in Computer Science classes are frequently attributed to lack of student engagement and motivation. Meanwhile, gamification increasingly attracts the interest of educators due to its potential to foster motivation and behavioral changes in learning contexts. This workshop introduces participants to the concept of gamification and the use of OneUp Learning, a platform aimed at gamifying academic courses. OneUp is aimed at encouraging students' out-of-class practicing and increasing their overall engagement in the course through employing game design elements. The platform is course-independent and highly configurable, allowing the instructor to turn on and off a number of gamification features, such as avatars, points (challenge and skill points), leaderboards, skill boards, a progress bar, content unlocking, goal-setting, badges, virtual currency, duels, chat, etc. For using OneUp, an instructor has to provide practice exercises and to specify the gamification elements to be used. Apart from the standard question types, OneUp supports Parson's problems and dynamic problems (e.g. programming problems). The workshop proceeds in two sessions. In the first session we introduce the OneUp platform and present our experience of gamifying a Data Structures course. In the second, workshop attendees engage in hands-on experience: we show them how to use OneUp in their classes. In particular, we assist them in creating problems and challenges, configuring desired gamification features, defining their own badges and rules for earning and spending virtual currency, and monitoring the engagement and progress of their students. Laptops required.

Gamification in education still lacks systematic studies assessing its impact on learning. This poster will present the design of our empirical studies on the effect of various combination of game elements on student motivation and engagement in CS courses.

Big data and cloud computing collectively offer a paradigm shift in the way businesses are now acquiring, using and managing information technology. This creates the need for every CS student to be equipped with foundational knowledge in this collective paradigm and to possess some hands-on experience in deploying and managing big data applications in the cloud. We argue that, for substantial coverage of big data and cloud computing concepts and skills, the relevant topics need to be integrated into multiple core courses across the undergraduate CS curriculum rather than creating additional standalone core or elective courses and performing a major overhaul of the curriculum. Our approach to including these topics is to develop autonomous learning modules for specific core courses in which their coverage might find an appropriate context. In this paper, three such modules are discussed and our classroom experiences during these interventions are documented. So far, we have achieved reasonable success in attaining student learning outcomes, enhanced engagement, and interests. Our objective is to share our experience with the academics who aim at incorporating similar pedagogy and to receive feedback about our approach.

Having to compete harder than ever for students' attention against cool social media sites, mobile apps, video games, messaging, etc., instructors are struggling to find new ways to motivate and engage learners. Gamification offers a promising framework for educational interventions that can increase students' motivation and engagement. This BOF will bring together instructors who have already explored the use of various game driven strategies and elements to increase the motivation of their students and also those who are looking for promising interventions to do that. The facilitators conducted a similar BOF at SIGCSE 2018, which was well attended, well received and sparked an interesting and lively discussion. This BOF will continue the conversation on what might be useful ways of incorporating game elements in Computer Science courses, what tools might be needed to support such efforts, how can the student experience of gamified learning be personalized, etc. Two NSF sponsored projects related to the use of gamification in STEM courses will seed the discussion. We hope to collectively contribute to the ongoing important discussion on successful strategies for applying of gamification in Computer Science education.

Although many CS courses require extensive practice, a large number of students show low motivation for engaging in non-graded, self-directed learning activities. To address this problem, we developed OneUp - a highly configurable course gamification platform that enables instructors to tailor the gamification features to fit their preferences. This paper presents a case study of using OneUp to gamify a Data Structures course. The focus is on encouraging students' self-study and better engagement with out-of-class online practicing. We describe the utilized game elements - badges, leaderboard, virtual currency, and learning dashboards, and provide a descriptive analysis of their use. The results of our evaluation show that this gamification intervention has been well received by the students, resulting in significantly increased student engagement and out-of-class practicing and in a reduced failing rate.

The low performance and drop-outs in Computer Science classes are frequently attributed to lack of engagement and motivation. Meanwhile, gamification - the application of game design principles and game mechanics to a non-game context - increasingly attracts the interest of educators due to its potential to foster motivation and behavioral changes in learning contexts. This Demo will introduce OneUp Learning, a platform aimed at facilitating the gamification of academic courses. OneUp enables instructors to define course activities and create exercise problems for practicing and self-assessment, as well as exams or quizzes for testing particular skills. The platform enables gamifying these practice activities. It is highly configurable and supports tailoring gamification features to meet the vision of the instructor. The gamification related configuration includes the choice of the game elements to be used along with specification of gaming rules for them. The system currently supports the following game elements: points (challenge points, skill points, and activity points), progress bar, virtual currency, badges, leaderboard, skill board, learning dashboard, and avatars. In the demo, we will show interested instructors how they can use OneUp in their classes. In particular, we will show them how to create problems and challenges, how to configure the gamification features they like to use, how to define their own badges, how to define rules for earning and spending virtual currency (in the course shop), and how to monitor the engagement and progress of their students. This work is supported by the NSF under Grants HBCU-UP TIP-1623236 and DUE-1821189.

Most of the current educational gamification applications are created without clear connections to motivational theories backing their design. As a result, they focus more on rewards and progress information and less on gameful experiences. This paper discusses the design of OneUp Learning - a gamification platform developed with the aim to address these problems. It discusses the underlying motivational theory and psychological mechanisms of games driving its design and briefly describes the functionality of the resulting highly configurable platform for gamifying academic courses and studying the motivational mechanisms of educational gamification.

There is good evidence that students learn better when given more opportunity to practice skills using related problems. However, this requires a sufficient supply of automatically graded problems to enable instant feedback. This can be achieved through automating the process of problem generation. While a few dynamic problem generation systems exist, they are either very specific to a single topic (such as tools for automatic generation of parameterized questions for Java or C programming or they are intended for other disciplines and not easily adapted to the needs of Computer Science. We have developed a prototype system for authoring, administering, and grading dynamic problems. This system is specifically designed for computer science. To this end, it supports complex logic, calling external programs such as compilers or databases, and the creation and manipulation of figures and diagrams. Problems and useful code libraries can be created and shared between instructors. It is a web-based system where instructors can specify problems by combining static text or images with bits of Lua code which add dynamism. When students use the system, their answers will be graded automatically, and they will be able to see the results, thus giving them quicker feedback. This is an integrated portion of a larger gamified learning platform called OneUp which is under development and aims to combine hands-on practicing with additional game-like motivational mechanisms. The goal of both the larger platform and the dynamic problems in specific is to increase student engagement in the learning process.

User obligations are actions that the human users are required to perform in some future time. These are common in many practical access control and privacy and can depend on and affect the authorization state. Consequently, a user can incur an obligation that she is not authorized to perform which may hamper the usability of a system. To mitigate this problem, previous work introduced a property of the authorization state, accountability, which requires that all the obligatory actions to be authorized when they are attempted. Although, existing work provides a specific and tractable decision procedure for a variation of the accountability property, it makes a simplified assumption that no cascading obligations may happen, i.e., obligatory actions cannot further incur obligations. This is a strong assumption which reduces the expressive power of past models, and thus cannot support many obligation scenarios in practical security and privacy policies. In this work, we precisely specify the strong accountability property in the presence of cascading obligations and prove that deciding it is NP-hard. We provide for several special yet practical cases of cascading obligations (i.e., repetitive, finite cascading, etc.) a tractable decision procedure for accountability. Our experimental results illustrate that supporting such special cases is feasible in practice.

This paper is part of a project investigating authorization systems that assign obligations to users. We are particularly interested in obligations that require authorization to be performed and that, when performed, may modify the authorization state. In this context, a user may incur an obligation she is unauthorized to perform. Prior work has introduced a property of the authorization system state that ensures users will be authorized to fulfill their obligations. We call this property accountability because users that fail to perform authorized obligations are accountable for their non-performance. While a reference monitor can mitigate violations of accountability, it cannot prevent them entirely. This paper presents techniques to be used by obligation system managers to restore accountability. We introduce several notions of dependence among pending obligations that must be considered in this process. We also introduce a novel notion we call obligation pool slicing, owing to its similarity to program slicing. An obligation pool slice identifies a set of obligations that the administrator may need to consider when applying strategies proposed here for restoring accountability. The paper also presents the system architecture of an authorization system that incorporates obligations that can require and affect authorizations.

Many authorization system models include some notion of obligation. Little attention has been given to user obligations that depend on and affect authorizations. However, to be usable, the system must ensure users have the authorizations they need when their obligations must be performed. Prior work in this area introduced accountability properties that ensure failure to fulfill obligations is not due to lack of required authorizations. That work presented inconclusive and purely theoretical results concerning the feasibility of maintaining accountability in practice. The results of the current paper include algorithms and performance analysis that support the thesis that maintaining accountability in a reference monitor is reasonable in many applications.

Though a user's privileges are often granted based on the tasks that the user is expected to fulfill, the concept of tasks is usually not explicitly modeled in access control. We propose a system where tasks are the central concept that associates users to privileges. Ideally a user should be able to utilize these privileges and fulfill his tasks, but not to take harmful actions. To ensure this, a system often specifies a high-level security property to restrict the sequence of actions that a user can perform. In this paper, we propose a general model of access control in task-based system. This model considers the permissions a user as well as their temporal availability. Based on this model, we investigate the problem of enforcing security properties both statically (i.e., when tasks are assigned) and dynamically (i.e., when actions are performed). We study the complexity of static enforcement, and design efficient dynamic enforcement algorithms that avoiding unnecessary history tracking.

Traditional security policies largely focus on access control requirements, which specify who can access what under what circumstances. Besides access control requirements, the availability of services in many applications often further imposes obligation requirements, which specify what actions have to be taken by a subject in the future as a condition of getting certain privileges at present. However, it is not clear yet what the implications of obligation policies are concerning the security goals of a system.In this paper, we propose a formal metamodel that captures the key aspects of a system that are relevant to obligation management. We formally investigate the interpretation of security policies from the perspective of obligations, and define secure system states based on the concept of accountability. We also study the complexity of checking a state's accountability under different assumptions about a system.

Automated trust negotiation is an approach which establishes trust between strangers through the bilateral, iterative disclosure of digital credentials. Sensitive credentials are protected by access control policies which may also be communicated to the other party. Ideally, sensitive information should not be known by others unless its access control policy has been satisfied. However, due to bilateral information exchange, information may flow to others in a variety of forms, many of which cannot be protected by access control policies alone. In particular, sensitive information may be inferred by observing negotiation participants' behavior even when access control policies are strictly enforced.In this paper, we propose a general framework for the safety of trust negotiation systems. Compared to the existing safety model, our framework focuses on the actual information gain during trust negotiation instead of the exchanged messages. Thus, it directly reflects the essence of safety in sensitive information protection. Based on the proposed framework, we develop policy databases as a mechanism to help prevent unauthorized information inferences during trust negotiation. We show that policy databases achieve the same protection of sensitive information as existing solutions without imposing additional complications to the interaction between negotiation participants or restricting users' autonomy in defining their own policies.

Using alternative cache indexing/hashing functions is a popular technique to reduce conflict misses by achieving a more uniform cache access distribution across the sets in the cache. Although various alternative hashing functions have been demonstrated to eliminate the worst case conflict behavior, no study has really analyzed the pathological behavior of such hashing functions that often result in performance slowdown. We present an in-depth analysis of the pathological behavior of cache hashing functions. Based on the analysis, we propose two new hashing functions: prime modulo and prime displacement that are resistant to pathological behavior and yet are able to eliminate the worst case conflict behavior in the L2 cache. We show that these two schemes can be implemented in fast hardware using a set of narrow add operations, with negligible fragmentation in the L2 cache. We evaluate the schemes on 23 memory intensive applications. For applications that have nonuniform cache accesses, both prime modulo and prime displacement hashing achieve an average speedup of 1.27 compared to traditional hashing, without slowing down any of the 23 benchmarks. We also evaluate using multiple prime displacement hashing functions in conjunction with a skewed associative L2 cache. The skewed associative cache achieves a better average speedup at the cost of some pathological behavior that slows down four applications by up to 7%.