Underrepresentation of women in computer science (CS) increasingly demands the necessity to find and enhance current learning engagement approaches to bring more women into computing fields. Some researchers have been exploring the influence of gamification on female students as one of these possible learning engagement strategies. Gamification refers to the introduction of video game elements into non-game activities to enhance engagement and motivation. Previous studies have reported mixed results of the impact of gamification on women. In this study, we introduce SEP-CyLE (Software Engineering and Programming Cyberlearning Environment), an online gamified tool that was designed to provide supplemental computing content to students. This paper presents a convergent mixed-methods study guided by social identity theory and self-efficacy to understand women's experiences with this gamified tool. More specifically, this study explores virtual points' and leaderboards' effects on CS identity development, self-efficacy, and performance. The results show that virtual points and the leaderboard contributed to improved performance for students of all genders, suggesting that gamification is a gender-neutral learning engagement strategy that improves female students' performance as much as male students. Regardless of improved performance, most women did not actively enjoy or were motivated by the virtual points or leaderboard in SEP-CyLE. Additionally, gamification had no significant impact on CS identity development or self-efficacy constructs and had little to no impact on women's interest and engagement in the field of computing.

Gamification in education: a mixed-methods study of gender on computer science students’ academic performance and identity development

The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time. As government policy and health measures shift, public perceptions and concerns also change, an evolution documented within discourse on social media. We propose a dynamic content-specific LDA topic modeling technique that can help to identify different domains of COVID-specific discourse that can be used to track societal shifts in concerns or views. Our experiments show that these model-derived topics are more coherent than standard LDA topics, and also provide new features that are more helpful in prediction of COVID-19 related outcomes including mobility and unemployment rate.

Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling

Energy-conscious flexible job shop scheduling problem considering transportation time and deterioration effect simultaneously

To date, extensively high demand for hydrogen peroxide (H2O2) has been predominantly supplied by the anthraquinone process for several worldwide applications, encompassing wastewater treatment, environmental remediation, and chemical synthesis. However, the compacted manufacturing, massive energy input and the release of tremendous wastes have restricted commercialization feasibility. Regards to mitigate such issues, the photocatalytic H2O2 production by utilizing g-C3N4 catalysts has endowed a greener, sustainable and promising alternative, considering that it involves water and oxygen as reactants in the present of sunlight as energy input. Herein, we have manifested a comprehensive overview of the research progress for g-C3N4-based semiconductors for photocatalytic H2O2 generation. This review has systematically elucidated state-of-the-art development of different modifications on g-C3N4 to unravel the fundamental mechanism of H2O2 evolution via oxygen reduction reaction (ORR) and water oxidation reaction (WOR). In addition, the contribution made by vacancy introduction, doping, heterogenization, and co-catalyst passivation with respect to photoefficiency enhancement have been clarified. Furthermore, the current challenges and perspective of future development directions on photocatalytic H2O2 production have also been highlighted. As such, g-C3N4 stands as the next step toward advancement in the configuration and modulation of high-efficiency photocatalysts.

Recent advances in structural modification on graphitic carbon nitride (g-C
 3N
 4)-based photocatalysts for high-efficiency photocatalytic H
 2O
 2production

Kinetics and adsorption performance of biosorbent starch/poly(vinyl alcohol)/graphene oxide nanocomposite for the removal of dyes

In recent years, g-C3N4@MXene photocatalysts have received much attention due to their special composition and excellent properties. MXenes consisting of transition metal carbides, nitrides, and carbonitrides derived from the MAX phase are used as cocatalysts or g-C3N4 (GCN) supporting composites in a variety of photocatalytic processes that accelerate the separation of charge carriers with their heterojunction structure. In addition to the high ability of g-C3N4@MXene nanocomposite to absorb light, it has high photocorrosion resistance in the processes of hydrogen evolution, wastewater treatment, nitrogen fixation, NO treatment, and oxidation and reduction photoreactions. In this review, the latest developments and new technologies for the manufacture and application of noble metal-free g-C3N4@MXene nanocomposite have been discussed and the future perspective has been drawn to deal with challenges related to energy and the environment.

New strategies in the preparation of binary g-C3N4/MXene composites for visible-light-driven photocatalytic applications

Sequential Treatment of Textile Industry Wastewater Using Electrocoagulation and Photo electro-Fenton Processes

Recent studies of student problem-solving behavior have shown stable behavior patterns within student groups. In this work, we study patterns of student behavior in a richer self-organized practice context where student worked with a combination of problems to solve and worked examples to study. We model student behavior in the form of vectors of micro-patterns and examine student behavior stability in various ways via these vectors. To discover and examine global behavior patterns associated with groups of students, we cluster students according to their behavior patterns and evaluate these clusters in accordance with student performance.

Annotated Examples and Parameterized Exercises: Analyzing Students’ Behavior Patterns

Nowadays, distributed systems are prevalent and practical in network environments. In distributed systems, pattern recognition help to extract information from network nodes. Meanwhile, data mining in such systems needs resource consideration in terms of storage and computational time. The primary requirement of these systems is a scalable mechanism to distribute the tasks on several databases. Moreover, to do a centralized process, relocating data from all nodes or partial nodes to a central node has confidential risks and traffic overhead. Therefore, distributed data mining in distributed environments needs systematic and structural techniques. In this paper, we propose a new algorithm to extract frequent itemsets in Wireless Sensor Networks. Through this algorithm, nodes frequent local itemsets are obtained with a Bitwise approach, and nodes are classified into clusters by using the Low Energy-Adaptive Clustering Hierarchy (LEACH) algorithm. Connecting the head cluster is performed by a Gossip-based protocol to achieve the values of global support, and it finally resulted in the extraction of frequent itemsets. The proposed algorithm has been simulated in various scenarios using Java software, and algorithm efficiency is evaluated in terms of execution time and average accuracy. Our algorithm is compared with a Gossip-based algorithm, and then some improvements in execution time have been presented.

/pdf/distributed-frequent-itemset-mining-with-bitwise-method-and-3fkqusf2u6.pdf

Distributed Frequent Itemset Mining with Bitwise Method and Using the Gossip-Based Protocol

Modern online learning platforms offer a wealth of learning content while leaving the choice of content for study and practice to the learner. Recent work has demonstrated that many students use inefficient learning strategies that lead to lower performance in this context. The ability to detect inefficient learning behavior by monitoring learning data opens a way to timely intervention that could lead to better learning and performance. In this work, we propose SB-DNMF, a structure-based discriminative non-negative matrix factorization model aimed to distinguish between common and distinct learning behavior patterns of low- and high-learning gain students. Our model can discover latent groups of students’ behavioral micro-patterns while accounting for the structural similarities between these micro-patterns based upon a weighted edit-distance measure. Our experiments demonstrate that SB-DNMF can find meaningful latent factors that are associated with students’ learning gain and can cluster the behavioral patterns into common (trait), and performance-related groups.

Structure-Based Discriminative Matrix Factorization for Detecting Inefficient Learning Behaviors

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