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A well-designed educational programming dataset is a valuable asset for students and educators. Such a dataset enables students to improve their programming performances continuously, provides researchers with significant data sources to identify students' learning behaviours and enhance the quality of programming education. Several existing datasets for programming education are either limited by a small number of participating students or a short span of learning records, bringing great challenges to investigate students' learning patterns in programming. We present a graph-based large-scale dataset specialized in programming learning on Online Judge (OJ) platform. The dataset, named ACcoding, was built by a university teaching group. As of the submission date of the initial manuscript of this paper (May 6, 2022), the dataset contains 4,046,652 task-solving records submitted by 27,444 students on 4,559 programming tasks over a span of 6 years. The large size of the dataset, combined with rich functional features, empowers educators to trace students' programming progress and choose appropriate programming tasks for specific training purposes. We also presents examples of applications used by the dataset. [ABSTRACT FROM AUTHOR]

Due to increasingly global cultural exchanges, people are more conscious about the importance of mastering a foreign language. Language translation and helping students in the colleges with their learning abilities to master one or more languages using computer-aided translation and training system has become an important research. Intelligibility is one of the most important indicators for translation evaluation of Chinese language spoken by English learners, but there is a big gap between the automatic translation intelligibility score and the human expert score. Based on this concept, this paper addresses the existing challenges faced by translation detection software to analyze their limitations and characteristics, especially related to learners' Chinese translation experience. To enhance translation recognition accuracy, we employ a data mining approach to identify imaging factors associated with incorrect translation by college students. First, we develop a data mining recognition and detection method, leading to improved accuracy in translation recognition by utilizing association specification and classification. The classifier is then utilized to distinguish various types of translation errors, while also incorporating a translation intelligibility evaluation to provide error correction feedback to learners. The proposed model outperforms standard translation methods and alternative approaches, achieving impressive identification accuracy (94.58%), efficiency (88.12%), and a low missed detection rate (5.32%). Moreover, it significantly reduces the translation mistakes, including grammatical (5.21%), semantic (3.45%), and contextual (2.18%) errors. The support vector machine (SVM) classifier outshines artificial neural network (ANN) classification in error detection, and the suggested scoring system demonstrates its superiority with the highest average correlation coefficient (0.822) compared to expert scoring. Experimental results confirm the improved accuracy and effectiveness of the proposed translation recognition and detection method. [ABSTRACT FROM AUTHOR]

Due to the COVID-19 pandemic, many social work students and educators had to switch gears quickly and replace face-to-face courses with online delivery. While most had had experience with remote learning, the unexpected and immediate transition was challenging. Students and educators who had chosen in-person instruction had to adapt quickly to a learning paradigm for which they had not planned, while simultaneously coping with the anxieties brought on by the pandemic, such as economic hardships, threat of illness, and new family responsibilities. The pandemic has engendered fear, trauma, grief, and loss, all of which negatively affect instruction and learning. This reflection paper identifies special challenges and issues with regard to teaching and learning in social work clinical practice courses brought on by the pandemic. Utilizing the theoretical frameworks of ambiguous loss, interpersonal neurobiology, and the here and now approach, this paper suggests effective teaching methods and collaborative learning strategies to inform social work education during academic disruption in this and future emergencies (e.g., natural disasters). It is suggested that social presence, as well as group cohesion among students and between students and instructors, can serve as a protective factor to ensure continued productive motivation for teaching and learning while facing the challenges that are experienced during such times. [ABSTRACT FROM AUTHOR]

This paper explores the potential of edge computing and artificial intelligence to improve the quality evaluation of physical education (PE) teaching in ordinary colleges. Unlike other discipline-based teaching, PE lacks fixed classrooms, materials, and tasks, making it difficult to uniformly evaluate teaching quality. To address this issue, an edge computing optimization model is proposed to optimize the PE quality evaluation model. The evaluation of physical education curriculum teaching is a critical component of physical education teaching work, directly affecting various aspects of teaching activities. The proposed teaching evaluation model focuses on the physical fitness compliance rate of students' evaluation and reweights the proportion of final examination scores. In addition, a PE teaching health system is designed to monitor teaching quality in real-time, allowing students to evaluate the entire learning process. The system is optimized using edge computing technology, reducing network transmission costs by 20% and increasing transmission efficiency by 15%. Experimental results demonstrate that the proposed system effectively collects student evaluations and improves the scientific nature of PE teaching goals. The integration of edge computing and artificial intelligence technologies has the potential to enhance the capabilities of the system and improve teaching quality in ordinary colleges. [ABSTRACT FROM AUTHOR]

University teaching practices impact student interest, engagement, and academic performance. This paper presents a study that uses artificial intelligence (AI) to examine students' preferences for university teaching practices. We asked students in various fields open-ended questions about the best teaching practices they had experienced. Due to the large amount of data obtained, we used the AI-based language model Generative Pretrained Transformer-3 (GPT-3) to analyse the responses. With this model, we sorted students' testimonies into nine theory-based categories regarding teaching practices. After analysing the reliability of the classifications conducted by GPT-3, we found that the agreement between humans was similar to that observed between humans and the AI model, which supported its reliability. Regarding students' preferences for teaching practices, the results showed that students prefer practices that focus on (1) clarity and (2) interaction and relationships. These results enable the use of AI-based tools that facilitate the analysis of large amounts of information collected through open methods. At the didactic level, students' preferences and demand for clear teaching practices (in which ideas and activities are stated and shown without ambiguity) that are based on interaction and relationships (between teachers and students and among students themselves) are demonstrable. [ABSTRACT FROM AUTHOR]

An increase in online and hybrid education during and after the Covid-19 pandemic has rapidly accelerated the infiltration of digital media into mainstream university teaching. Global challenges, such as ecological crises, call for further radical changes in university teaching, requiring an even richer convergence of 'natural,' 'human' and 'digital'. In this paper, we argue that this convergence demands us to go beyond 'the great online transition' and reframe how we think about university, teachers' roles and their competencies to use digital technologies. We focus on what it takes to be a teacher in a sustainable university and consider emerging trends at three levels of the educational ecosystem—global developments (macro), teachers' local practices (meso), and daily activities (micro). Through discussion of examples of ecopedagogies and pedagogies of care and self-care, we argue that teaching requires a fluency to embrace different ways of knowing and collective awareness of how the digital is entwined with human practices within and across different levels of the educational ecosystem. For this, there is a need to move beyond person-centric theorisations of teacher digital competencies towards more holistic, ecological conceptualisations. It also requires going beyond functionalist views of teachers' roles towards enabling their agentive engagement with a future-oriented, sustainable university mission. [ABSTRACT FROM AUTHOR]

With the development of wearable device technology, light sensor has been applied in the field of fitness, but in the aerobics teaching in colleges and universities, the use of light sensor technology for motion capture research is still limited. In order to monitor the aerobic exercise process of the students in real time, the experiment converts the movement into the corresponding data information through the sensor, and then the computer is combined with the projector or camera equipment for visual simulation. Therefore, this paper aims to design and develop a wearable device-based light sensor motion capture system for calisthenics teaching in colleges and universities. The research uses light sensor technology to monitor students' movements in real time by integrating light sensor devices into their wearable devices. Sensors collect data about body posture, range of motion, and speed, and transmit it to a virtual system for processing and analysis. Through the analysis of experimental data, it is found that the motion capture system based on light sensor has high accuracy and real-time in the aerobics teaching in colleges and universities. Students can get real-time feedback on posture, amplitude and speed to help them better master the technique of aerobics. [ABSTRACT FROM AUTHOR]

The calls for actions to combat climate change have resulted in renewable energy adoption and, consequently, an emerging renewable energy industry. Of course, this has also precipitated the need for more renewable energy expert grounded in hybrid renewable energy system (HRES) software to support the growing industry. Hence, selecting particular software for teaching renewable energy modules for the training of students at a university level is essential. The appraisal and selection of HRES sizing and optimisation software have become crucial for instructors and learners being prepared for the industry. This paper's central contribution is to provide academics, tutors, and researchers in the field of renewable energy system sizing with an idea on how to proficiently and effectively deploy multi-criteria decision-making (MCDM) approaches in HRES sizing and optimisation software selection problems. A framework that combines fuzzy entropy method and fuzzy-VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) was used to rank HRES simulation software. The performance of the framework was compared with that of the complex proportional assessment (COPRAS) method. The article observed a difference between the fuzzy VIKOR and COPRAS solution for ranking the most preferred software; the most preferred option using the Fuzzy VIKOR is the BCHP screening tool, and HOMER for the COPRAS method. They ranked the least preferred software as RETScreen (VIKOR method) and ORCED (COPRAS method). [ABSTRACT FROM AUTHOR]

There is a growing interest in contemplative practices in higher education. Researchers have explored the use of meditation to address the increasing requests for counseling and as a resource for improving student mental health. Contemplative practices have also been incorporated into the fundamental learning objectives in some US universities. This study of teaching methods uses first-person narratives from twenty-three 10-week seminars to examine how meditation practice benefited undergraduate students at a large public university in the US. The seminar, "Contemporary American Buddhism: How Meditation Became a Part of the Mainstream," provided meditation instruction within an historical and cultural context and was taught over a nine-year period. Four hundred undergraduates' reflective writings were included in this study. During the seminars, students participated in structured in-class meditations, practiced mindfulness exercises, read contemporary texts, viewed recent media and wrote reflective exercises. Students learned how meditation has been adapted for contemporary society, engaged in critical thinking, and reflected on their experiences. An examination of their narrative self-reports indicates outcomes similar to studies using highly structured meditation protocols; an improvement in overall well-being, lessened anxiety, better focus and increased emotional control. This study of teaching methodology also shows that students expressed a desire to continue the practice once the seminar was complete. The results suggest that a structured and systematic investigation of this teaching model would add significantly to the discussion on how to teach contemplative practice to college students. [ABSTRACT FROM AUTHOR]

In order to effectively extract the multi-dimensional data of teaching quality evaluation and accurately evaluate the quality of network distance teaching quality in universities, an optimal classification algorithm for network distance teaching quality evaluation is proposed. From the perspectives of teaching attitude, teaching skill, teaching skill, teaching content, teaching method and means, the quality evaluation indices of network distance teaching is designed. Combined with the evaluation indices, the multi-dimensional data mining method based on OLAP technology is used to mine the required multi-dimensional data of teaching quality evaluation in the distance teaching data warehouse of universities, the required multi-dimensional data is input into SVM algorithm to solve the optimal classification hyperplane of multi-dimensional data and implement the optimal classification of multi-dimensional data. The quality of distance teaching is evaluated by improving the salp group algorithm and setting the penalty factor and kernel function of SVM algorithm. The experimental results show that the classification accuracy of this method for multidimensional data is over 90%, and the evaluation accuracy is as high as 99%, it can extract multi-dimensional teaching quality evaluation from the network distance teaching data, which has good classification effect and can improve the accuracy of network distance teaching quality evaluation. [ABSTRACT FROM AUTHOR]

Most universities are working committedly towards achieving the Sustainable Development Goals (SDG). The University of Alicante (UA) (Spain) has designed a series of strategies to help crystallize its aims within these goals, including those related to gender equality (SDG 5). Given the context, the aims of this study are (1) to find out whether teachers integrate SDG 5 into their teaching and, if so, what strategies they use, (2) to analyse how they perceive the effects of this and what factors help or hinder the process, and (3) to check for possible differences in these perceptions according to sex and faculty. Some 310 teachers from all the UA faculties participated. A descriptive and comparative analysis was performed, as was a content analysis. Half of the participants were found to be implementing these strategies, with women in general and teachers from the Faculty of Humanities in particular appearing to be most aware of the subject. However, only a minority applied them on a mainstream basis. The most used strategy was inclusive language. Other strategies, such as reflecting on equality, promoting equal participation, visibilizing women in different areas of knowledge, equal treatment of students and gender research, were also present in their narratives but to only a very small degree. Improved professional development for teachers and greater student awareness of gender issues were identified as positive effects of incorporating these strategies. Participants also felt that low awareness and a lack of time and training were obstacles to the process. Universities should therefore establish specific institutional policies aimed at integrating SDG 5 as a quality indicator. Training programmes should be redesigned to take into account a more practical approach, the type of learning (classroom, online or mixed), and subsequent follow-up in the classroom to guarantee effective integration. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic manifested around the World since February 2020, leading to disruptive effects on many aspects of people social life. The suspension of face-to-face teaching activities in schools and universities was the first containment measure adopted by the Governments to deal with the spread of the virus. Remote teaching has been the emergency solution implemented by schools and universities to limit the damages of schools and universities closure to students' learning. In this contribution we intend to suggest to policy makers and researchers how to assess the impact of emergency policies on remote learning in academia by analysing students' careers. In particular, we exploit the quasi-experimental setting arising from the sudden implementation of remote teaching in the second semester of academic year 2019/2020: we compare the performance of the cohort 2019/2020, which represents the treatment group, with the performance of the cohort 2018/2019, which represents the control group. We distinguish the impact of remote teaching at two levels: degree program and single courses within a degree program. We suggest to use Difference-In-Differences approach in the former case and multilevel modeling in the latter one. The proposal is illustrated analysing administrative data referred to freshmen of cohorts 2018/2019 and 2019/2020 for a sample of degree programs of the University of Florence (Italy). [ABSTRACT FROM AUTHOR]

This study aims to develop an AI education policy for higher education by examining the perceptions and implications of text generative AI technologies. Data was collected from 457 students and 180 teachers and staff across various disciplines in Hong Kong universities, using both quantitative and qualitative research methods. Based on the findings, the study proposes an AI Ecological Education Policy Framework to address the multifaceted implications of AI integration in university teaching and learning. This framework is organized into three dimensions: Pedagogical, Governance, and Operational. The Pedagogical dimension concentrates on using AI to improve teaching and learning outcomes, while the Governance dimension tackles issues related to privacy, security, and accountability. The Operational dimension addresses matters concerning infrastructure and training. The framework fosters a nuanced understanding of the implications of AI integration in academic settings, ensuring that stakeholders are aware of their responsibilities and can take appropriate actions accordingly. Highlights: Proposed AI Ecological Education Policy Framework for university teaching and learning. Three dimensions: Pedagogical, Governance, and Operational AI Policy Framework. Qualitative and quantitative data collected from students, teachers, and staff. Ten key areas identified for planning an AI policy in universities. Students should play an active role in drafting and implementing the policy. [ABSTRACT FROM AUTHOR]

This research used the qualitative multiple case study and phenomenological designs to explore how, without training, university teachers in Ghana managed large student numbers in the virtual environment during COVID-19. The study examined further the challenges the teachers faced in their virtual instructional delivery. Twelve participants drawn purposively from four large Ghanaian universities participated in individual interviews and follow-up virtual class observations. The findings revealed that the participants employed two management techniques in their virtual teaching—regulating the behaviour of learners and controlling instructional content. The research further uncovered that, although the teachers' complaints generally centred on environmental constraints and inadequate institutional support, those whose difficulties included using virtual tools did not have virtual teaching experience before the COVID period. The study supports the clarion call on university teachers involved in virtual teaching to personally seek a continual update of skills and competency in virtual delivery because it is an approach hinged on evolving technology. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic placed new teaching demands upon faculty that may have exacerbated existing race and gender disparities in the amount of emotional labor they perform. The present study surveyed 182 full-time tenured and tenure-track faculty from three small private liberal arts colleges to examine the effect of social and professional statuses on emotional labor (i.e., managing the expression of emotions to meet job requirements) during the emergency switch to remote instruction in spring 2020. Ordinary least squares (OLS) regression revealed that white cisgender men performed less emotional labor than Black, Indigenous, and People of Color (BIPOC) cisgender men, BIPOC cisgender women, and white cisgender women and gender non-conforming (GNC) faculty. Student demands for special favors fully mediated the relationship between intersectional race and gender identity and self-directed emotional labor and partially mediated its relationship with student-directed emotional labor. We conclude that the status shield afforded white cisgender men by their race and gender protected them from student demands that would have required them to engage in as much emotional labor as faculty with other intersectional race and gender identities during the pandemic. We discuss considering differences in emotional labor when making personnel decisions. [ABSTRACT FROM AUTHOR]

Preliminary results of a qualitative study of the lived experience of teaching and learning during the Covid-19 pandemic are presented. An instructor, a program director and five doctoral students in different stages of their coursework and dissertation proposal development, wrote a reflective journal. Participants varied in their levels of familiarity with technology-assisted education, personal backgrounds and circumstances including work and family responsibilities. Participants' journals documenting their reactions, struggles and coping since the abrupt move of the university from face to face to online classes were content analyzed. The analysis was co-conducted by five participants to identify themes and generate understanding of the experience. Two main themes emerged from the analysis: a developmental process of participants' reactions, perceptions and meaning making of the experience and factors that shaped it. Lessons learned are discussed and recommendations for professional education and directions for future research are suggested. [ABSTRACT FROM AUTHOR]

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In March 2020, due to the COVID19 virus that is spreading throughout the world, Spain lives an anomalous situation concerning the normal course of basic, secondary, and higher education. On March 2nd, 2020, the state authorities announced the end of face-to-face teaching in schools and universities. Then nothing suggests that a week later all classes went canceled. However, this unusual fact does not end with the teaching–learning process, forcing all educational institutions, as well as the teaching community to reinvent themselves to continue with online teaching. This is the only possible way to do it. The authors must face a specific scenario to readapt the learning-teaching process. The way of teaching of the University of La Rioja is online but it will have to face some problems. The Polytechnic University of Madrid classroom education is compulsory, so there is a big gap between what teachers are used to and what they will have to face. This crisis makes us reflect on new technologies as necessary and essential tools in the new teaching–learning scenario presented. But it must also go beyond considering only the tools available to do it. Reflection must also consider the members of this process and those who eventually make possible education to take place. [ABSTRACT FROM AUTHOR]

To improve the efficiency and quality of college English teaching, we analyzed the feasibility and application process of data mining technology in college English teaching. The entire process of data classification mining was fully realized. A new teaching program was proposed. The object and target of data mining were determined. Online surveys were used to collect data. Data integration, data cleaning, data conversion, data reduction and other pre-processing technologies were adopted. The decision tree was generated by using the C4.5 algorithm, and the pruning was carried out. The result analysis decision tree model was completed. A detailed survey of the students' English learning in University was made in detail. The results showed that the qualified rate of students' English performance was increased from 20–30% to 50–60%. Therefore, the classification rules provide theoretical support for the school teaching decision. This method can improve the quality of English teaching. [ABSTRACT FROM AUTHOR]