This Full Research Paper investigated the correlations of automated formative feedback and student knowledge transfer in an advanced-level network course. A microservice of automated formative feedback was designed and implemented. The automated feedback was set up to address correctness and coding style along a series of milestones in finishing each of the assigned programming projects. 36 students participated in this study. Different from the concerns of prior studies that automated formative feedback may encourage trial & error and harm learning, this study identified a link between the automated formative feedback and student knowledge transfer. The results of this study confirmed positive findings from other fields on formative feedback, and call for more research on formative feedback in computing education.

Malicious attackers have been prevalent in online communities with the ability to create accounts freely and with ease on social platforms. This negatively impacts sub-communities on these platforms that often require trusted members especially when they are intended to be used for exchange of valuable goods or services. Prior research on identity deception detection on social platforms has focused on identifying features that are effective in detecting these accounts. These studies often utilize years of historical data to demonstrate the effectiveness of detection models. However, to this day, there has not been a classification and comparison of these detection features that have been used in past studies. Furthermore, for these features to become viable for wider implementation in large social platforms, a reduction in the volume of data necessary for the fine operation of these models is necessary. This paper proposes a proactive approach to detecting malicious accounts at the point of attempted entry into a sub-community. We constructed a taxonomy of features based on user data and provide a comparison of their efficacy as well as opportunities for reducing data requirements for these types of detection models. The features are generalized so that they can be applied to other social media platforms.

As the societal demands for application and knowledge in computer science (CS) increase, CS student enrollment keeps growing rapidly around the world. By continuously improving the efficacy of computing education and providing guidelines for learning and teaching practice, computing education research plays a vital role in addressing both educational and societal challenges that emerge from the growth of CS students. Given the significant role of computing education research, it is important to ensure the reliability of studies in this field. The extent to which studies can be replicated in a field is one of the most important standards for reliability. Different fields have paid increasing attention to the replication rates of their studies, but the replication rate of computing education was never systematically studied. To fill this gap, this study investigated the replication rate of computing education between 2009 and 2018. We examined 2,269 published studies from three major conferences and two major journals in computing education, and found that the overall replication rate of computing education was 2.38%. This study demonstrated the need for more replication studies in computing education and discussed how to encourage replication studies through research initiatives and policy making.

Efforts to improve machine learning performance begin with defining a valuable feature set. However, datasets with copious amounts of attributes can have relevant information that is obscured by its high dimensionality, which can be caused by repetitive characteristics or irrelevant qualities. Genetic algorithms provide improvements to feature sets through dimensionality reduction and feature construction. Most genetic algorithms follow the theoretical framework of evolutionary theory where a population of features randomly evolves through generations through a series of random operations such as crossover and mutation. While successful, the randomness of feature modification operations and derived constructed features may yield offsprings that under-perform compared to their ancestors, yet their properties are utilized in future generations. We developed a new genetic algorithm called Selective Equation Constructor (SEC) that evolves constructed features selectively in order to limit the shortcomings of other genetic algorithms. The algorithm leads to faster computation and better results compared to similar algorithms. Analysis of the results indicates increases in classification accuracy, decreased run time, and reduction in attribute count.

Cumulative experience is often seen as a major factor for influencing content quality in collaborative projects such as Wikipedia. However, past studies often utilize cumulative experience based on the quantity of work rather than quality and context. Moreover, the perspective on cumulative experience assumes a final destination for user behavior, whereas much of the literature indicates that user behavior changes over time. This paper aims to address these two factors by providing better descriptions and context to determine their effect on content quality. The study rematerialized these factors based on the quality of work and built a comprehensive model of how article quality materializes through groups of users. Regression models in this study indicate that recent behavior is a more powerful predictor of content quality change than cumulative experience. The implications of the findings impact the design of task-routing systems as well as designers and group managers’ work in collaborative projects.

Information and communication technologies (ICTs) provide a distinctive structure of opportunities with the potential to promote political engagement. However, concerns remain over unequal technological access in our society, as political resources available on the internet empower those with the resources and motivation to take advantage of them, leaving those who are disengaged farther behind. Hence, those who face digital inequalities are not only deprived of the benefits of the so‐called Information Society, they are also deprived of exercising their civic rights. To promote political engagement among the marginalized, we analyze different sociotechnical factors that may play a role in promoting their inclusion in future political activities. We employed a survey for marginalized communities to analyze a set of research questions relating to sociotechnical factors. We show that online content creation, digital freedom, and access to the mobile Internet may positively impact political engagement. The development of these factors may not only promote the inclusion of marginalized populations in future political events, but also help to build a more equal society where everyone's voice has a chance to be heard.

Identity deception in social media applications has negatively impacted online communities and it is likely to increase as the social media user population grows. The ease of generating new accounts on social media has exacerbated the issue. Many previous studies have been posited that focused on both verbal, non-verbal, and network data produced by users in an attempt to detect identity deception. However, although these methods produced a high accuracy, they are mainly reactive to the issue of identity deception. This paper proposes a proactive approach that leverages social network data and it is focused on identity deception prevention for online sub-communities, communities that exist within larger communities (e.g., Facebook groups or Subreddits). The method can be applied to various types of social media applications and produces high accuracy in identifying deceptive accounts at the time of attempted entry to a sub-community. Performance results as well as limitations for the method are presented. A discussion follows on the identification of possible implications of this paper for social media applications and future directions on deception prevention are proposed.

Online collaborative projects have been utilized in a variety of ways over the past decade, such as bringing people together to build open source software or developing the world's largest free encyclopedia. Personal communication networks as a feature do not exist in all collaborative projects. It is currently unclear if a designer's decision to include a personal communication network in a collaborative project's structure affects outcome quality. In this study, I investigated Wikipedia's personal communication network and analyzed which Wikipedia editors are utilizing it and how they are connected to outcome quality. Evidence suggests that people who utilize these networks are more experienced in editing high quality articles and are more integrated in the community. Additionally, these individuals utilize the personal communication network for coordinating and perhaps mentoring editors who edit lower quality articles. The value of these networks is demonstrated by the characteristics of the users who use them. These findings indicate that designers of online collaborative projects can help improve the quality of outcomes in these projects by deciding to implement a personal communication network in their communities.

Identity deception has become an increasingly important issue in the social media environment. The case of blocked users initiating new accounts, often called sockpuppetry, is widely known and past efforts, which have attempted to detect such users, have been primarily based on verbal behavior (e.g., using profile data or lexical features in text). Although these methods yield a high detection accuracy rate, they are computationally inefficient for the social media environment, which often involves databases with large volumes of data. To date, little attention has been paid to detecting online deception using nonverbal behavior. We present a detection method based on nonverbal behavior for identity deception, which can be applied to many types of social media. Using Wikipedia as an experimental case, we demonstrate that our proposed method results in high detection accuracy over previous methods proposed while being computationally efficient for the social media environment. We also demonstrate the potential of nonverbal behavior data that exists in social media and how designers and developers can leverage such nonverbal information in detecting deception to safeguard their online communities.

Groupthink behavior is always a risk in online groups and group decision support systems (GDSS), especially when not all potential alternatives for problem resolution are considered. It becomes a reality when individuals simply conform to the majority opinion and hesitate to suggest their own solutions to a problem. Anonymity has long been established to have an effect on conformity, but no previous research has explored the effects of different anonymity states in relation to an individual's likelihood to conform. Through a survey of randomly chosen participants from the English‐language Wikipedia community, I explored the effects of anonymity on the likelihood of conforming to group opinion. In addition, I differentiated between actual states of anonymity and individuals' perceptions of anonymity. His findings indicate that although people perceive anonymity differently depending on their anonymity state, different states of anonymity do not have a strong effect on the likelihood of conforming to group opinion. Based on this evidence, I make recommendations for software engineers who have a direct hand in the design of online community platforms.