Evaluating lab assistant chatbot on student learning and behaviors in a programming short course

This research aims to study the relationship between university's students learning behavior affecting their educational academic performance by using the educational data mining method. The objective of this study is to identify the learning behavior of the university students through the data mining method. It is also to allow the academician to evaluate the university student's educational academic performance and to predict the university student's learning behavior affecting their educational academic performance in their university. Tinto's model is the main theoretical framework that studies the learning behavior of the student, together with emotional behavior factors to formulate a prediction of the student academic performance. It enables academician to analyze the characteristics of students enrolling into the universities such as their family background, their goals, priorities enrolling into universities and their experience they obtained. Positive result of this study has depicted the relationship between university student's behaviors affecting their academic performance. There is a significant positive correlation between student's learning behavior and their educational academic performance.

Student learning behavior not only shows student participation in the teaching process, but also affects the quality of teaching directly. However, manual observation and coding of student behaviors exist with high workload and are susceptible to subjective factors of analysts. Combining artificial intelligence and other technologies to achieve recognition and analysis of classroom student behavior is also a prevailing research trend. The current student behavior recognition system has problems such as failing to pay attention to the interference of teacher activates for student behavior recognition, fewer behavioral categories of students that can be recognized through visual information alone. Therefore, this paper develops a system of recognition of student learning behaviors in classroom teaching videos by a method on recognition of student learning behaviors based on audio-visual information to identify student learning behaviors in teaching videos. First, this paper designs a pre-annotation module in which the user marks the teacher's image, voice, and silent clips for providing accurate identification of subsequent student behaviors. Second, the student behavior recognition module provides for the recognition of eight categories of student behaviors in real classroom teaching videos and visualizes the results so that users can understand the percentage of student learning behaviors in the current teaching clip and the temporal changes of individual behavior categories. The purpose of system is to assist teachers to be able to count student learning behaviors in the classroom quickly and scientifically and grasp student learning dynamics for data-enabled classroom teaching analysis.

At the time of the COVID-19 pandemic in Indonesia, several learning changes occur in higher education, such as the implementation of online learning. This change is a concrete step for the government to overcome the spread of the COVID-19 virus in Indonesia. However, the change of learning to online has several obstacles in its implementation where both students and teachers are still not used to online learning. This study investigates student knowledge about covid 19 and its implications for student learning attitudes and behavior. An online survey using google forms to students of undergraduate and postgraduate programs at the School of Economics, Pasundan, Bandung, Indonesia, has as many as 247 students. The researchers used a nonparametric structural equation modeling approach to identify the effect of student knowledge about COVID-19 on attitudes and learning behavior. The study results identified that student knowledge had a positive influence on student attitudes and negatively affected learning behavior. This condition indicates that the increasing threat of covid 19, which students know, will be more positive in their actions; however, their learning behavior will decrease. For this reason, it is necessary to take comprehensive steps by the parties concerned to implement effective policies in reducing the impact of covid 19 to improve student learning behavior. The novelty of this research is that no research identifies a model of the influence of knowledge regarding the impact of covid 19 on student behavior and learning behavior in Indonesia.

From massive open online courses (MOOC) to the smaller scale use of learning management systems, many students interact with online platforms that are meant to support learning. Investigations into the use of these systems have considered how well students learn when certain approaches are employed. However, we do not fully understand how course type or system design influence student actions and experiences, meaning prior findings cannot be properly interpreted and used because we do not understand how these factors influence those findings. Accordingly, we conducted a study to compare student experiences and behaviors across learning management systems and courses from a learning analytics perspective. Differences in student behaviors and experiences highlight how system design and the nature of the course interact: Students reported increased learning support when using a system that foregrounds student interaction through discussion forums, but this relationship did not hold across all course types. Similarly, students from the content-delivery focused system spent more time online while feeling less supported regardless of which type of course they were taking. This newly found evidence for the often-interrelated influence that the course and system have on student experiences and behaviors should therefore be considered when selecting a system to meet particular pedagogical goals.

At the time of the COVID-19 pandemic in Indonesia, several learning changes occur in higher education, such as the implementation of online learning. This change is a concrete step for the government to overcome the spread of the COVID-19 virus in Indonesia. However, the change of learning to online has several obstacles in its implementation where both students and teachers are still not used to online learning. This study investigates student knowledge about covid 19 and its implications for student learning attitudes and behavior. An online survey using google forms to students of undergraduate and postgraduate programs at the School of Economics, Pasundan, Bandung, Indonesia, has as many as 247 students. The researchers used a nonparametric structural equation modeling approach to identify the effect of student knowledge about COVID-19 on attitudes and learning behavior. The study results identified that student knowledge had a positive influence on student attitudes and negatively affected learning behavior. This condition indicates that the increasing threat of covid 19, which students know, will be more positive in their actions; however, their learning behavior will decrease. For this reason, it is necessary to take comprehensive steps by the parties concerned to implement effective policies in reducing the impact of covid 19 to improve student learning behavior. The novelty of this research is that no research identifies a model of the influence of knowledge regarding the impact of covid 19 on student behavior and learning behavior in Indonesia.

Student interest in learning has been declining due to the increasing number of distractions. Interest is one of the important factors contributing to one’s desire and motivation to satisfy curiosity. As the times evolve, many things affect student interest in learning; one of which is smartphones equipped with the internet that promotes the ease of performing activities. Smartphones and the internet offer online games that can be accessed by anyone to play at any time, which can trigger online gaming addiction. The study aims to examine the interest in learning of nursing students who are addicted to online games. This study employed a qualitative method with a phenomenological approach. Data were collected through interviews, observations and documentation. The participants of the study were five students recruited using a snowball sampling method. Data were analyzed using Giorgi method. Results showed that interest in learning of students with online gaming addiction in the academic learning process varies. This can be seen through three emerging themes: student gaming behaviour, changes in students after online gaming, and the learning process of students with addiction to online games. It can be concluded that students’ online game behaviour is influenced by impulsivity that changes student behaviour and leads to addiction.

Exploring Students Behavior on Seating Arrangements in Learning Environment: A Review

We developed empirical models to test hypotheses that quizzes (announced or unannounced) can improve students' learning outcomes and enhance their investment in effort. The empirical analysis indicated support for the hypotheses. We further tested the hypothesis that these two different types of quizzes exert different influences on students' behaviour in effort investment and their learning outcomes. Consequently, evidence from an alternative demonstration indicated that these two types of quizzes could provide different impacts on students' effort investment and learning outcomes. Moreover, additional findings showed that quizzes not only provide a shorter-term learning goal - exam success - but also offer benefits for a longer-term learning goal - retaining information longer.

With the rapid development of educational technology, the application of deep learning in analyzing student behavior and psychological states has become a hot topic in the field of education. This study, grounded in deep learning technology, aims to explore the correlation between student learning behaviors and psychological atmosphere, as well as the positive impact of a constructive psychological atmosphere on student learning. The background section discusses the limitations of traditional educational assessment methods and the necessity and urgency of applying deep learning in education. The current state of study section presents the progress in analyzing the correlation between student behavior and psychological states, highlighting the shortcomings in data processing and model construction in existing studies. Addressing these shortcomings, this study proposes a student learning behavior detection scheme based on the lightweight neural network shufflenetV2 and validates through empirical study the positive influence of a constructive psychological atmosphere on student learning behavior. The results show that utilizing a lightweight network model can effectively identify patterns in student learning behavior and, to some extent, predict the students’ psychological states. Furthermore, a positive psychological atmosphere indeed enhances students’ learning motivation and behavior. The methods and findings of this study hold significant theoretical and practical implications for advancing personalized and intelligent education.

To model students' behavior and describe their behavior characteristics accurately and comprehensively, a framework for predicting students' learning performance based on behavioral model is proposed, which extracts features from multiple perspectives to describe behaviors more comprehensively, including statistical features and association features. In addition, a multi-task model is designed for fine-grained prediction of students' learning performance in the curriculum. A framework for predicting mastery based on online learning behavior is also put forward. Additional context information is added to the collaborative filtering algorithm, including student-knowledge-point mastery and class-knowledge-point, and students' mastery is predicted according to the learning path excavated. Considering the time-varying of mastery, the approximate curve of students' mastery of knowledge points is fitted according to the Ebinhaus forgetting curve. The experiments show that the proposed framework has a high recall rate for the prediction of learning performance, and also shows a certain practicability for early warning. Further, based on the model, the correlation between student behavior patterns and learning performance is discussed. The addition of additional information has improved the prediction efficiency, especially the operational efficiency. At the same time, the proposed framework can not only dynamically assess students' master of knowledge, but also facilitate the system to review feedback or adjust the learning order, and provide personalized learning services.

Classroom learning behavior analysis is an important means for teachers to understand the classroom situation, which helps teachers grasp the learning situation of each student, adjust teaching strategies, and then improve teaching quality. The article first collects lecture videos from real English classroom teaching in universities, summarizes six typical classroom behaviors, and designs and constructs a student classroom learning behavior dataset for the classroom learning behavior recognition model. In order to better integrate the spatio-temporal features of students’ classroom learning behaviors, an end-to-end student classroom learning behavior recognition model based on the dual-attention mechanism integrating spatio-temporal features (DA-YOWO) is designed. Through experimental comparison with YOLOv5 and FasterR-CNN models, it has been verified that the model in this paper has a high behavioral recognition accuracy for student learning behaviors in the English classroom. Then, in the real classroom scenario, the comprehensive weights of the student behavior indicators were determined by principal component analysis and factor analysis, and the analysis of student learning engagement was carried out. According to the results of the analysis, the learning status of each student in the English classroom was clarified so that the teachers had enough time to reflect on the deficiencies and problems in their teaching process according to the student’s performance in the classroom, which helped the teachers to better grasp the whole classroom.

Objectives . Automatic assessment systems are widely used to provide rapid feedback for students and reduce grading time. Despite the benefits of increased efficiency and improved pedagogical outcomes, an ongoing challenge is mitigating poor student behaviors when interacting with automatic assessment systems including numerous submissions, trial-and-error, and relying on marking feedback for problem solving. These behaviors negatively affect student learning as well as have significant impact on system resources. This research quantitatively examines how utilizing submission policies such as limiting the number of submissions and applying regression penalties can reduce negative student behaviors. The hypothesis is that both submission policies will have a significant impact on student behavior and reduce both the number of submissions and regressions in student performance. The research questions evaluate the impact on student behavior, determine which submission policy is the most effective, and what submission policy is preferred by students. Participants . The study involved two course sections in two different semesters consisting of a total of 224 students at the University of British Columbia, a research-intensive university. The students were evaluated using an automated assessment system in a large third year database course. Study Methods . The two course sections used an automated assessment system for constructing database design diagrams for assignments and exams. The first section had no limits on the number of submissions for both assignments and exams. The second section had limits for the exams but no limits on assignments. On the midterm, participants were randomly assigned to have either a restriction on the total number of submissions or unlimited submissions but with regression penalties if a graded answer was lower than a previous submission. On the final exam, students were given the option of selecting their submission policy. Student academic performance and submission profiles were compared between the course sections and the different submission policies. Findings. Unrestricted use of automatic grading systems results in high occurrence of undesirable student behavior including trial-and-error guessing and reduced time between submissions without sufficient independent thought. Both submission policies of limiting maximum submissions and utilizing regression penalties significantly reduce these behaviors by up to 85%. Overall, students prefer maximum submission limits, and demonstrate improved behavior and educational outcomes. Conclusions . Automated assessment systems when used for larger problems related to design and programming have benefits when deployed with submission restrictions (maximum attempts or regression penalty) for both improved student learning behaviors and to reduce the computational costs for the system. This is especially important for summative assessment but reasonable limits for formative assessments are also valuable.

The purpose of this study is to identify student learning behavior in online learning and to determine the relationship between student learning behavior and learning achievement based on learning history data (Learning logs) on the Learning Management System (LMS), including the performance of assignments and quizzes, utilization interaction features (forums and chat), as well as active access to learning resources (files and URLs). Pearson Correlation method is used to analyze the level of relationship between learning behavior and students’ achievement. The research object is 105 students at Muhammadiyah University of Enrekang who programmed introductory information technology (PTI) courses from 5 different classes but taught by the same lecturer. The total number of processed activity histories (after data preprocessing) is 6500 records, while the total number of logs before data preprocessing is 19386 records. Correlation analysis linking student behavior to student learning achievement is quite strong and unidirectional, as evidenced by the correlation value between learning behavior and student final grades which show an average number of 0.80 and all are positive, with confidence interval values reaching95%. This shows that the higher the learning activities that students participate in in online learning (the more active), the stronger the effect on student learning achievement. It also shows that student activity in completing assignments is the variable that most influences learning achievement with a correlation value of 0.88 (very strong).

Aim/Purpose: This study aimed to investigate burnout experienced by Thai nationals who teach English in a faith-based school. The study identified several specific challenges that caused burnout among these English teachers. Additionally, it examined the consequences of burnout on teacher performance, including their effectiveness in the classroom. The study also investigated what support is available for teachers that helped to prevent or reduce burnout. Introduction/Background: Teacher burnout is a global crisis, with education professionals experiencing higher exhaustion rates than workers in many other fields. In the United States, teacher burnout is 59%, compared to 48% among other professionals. This pattern extends to Asian countries, where teachers in China, Malaysia, and Thailand report significant emotional exhaustion and loss of autonomy. Thai teachers, in particular, struggle with mental health challenges, including depression, anxiety, and stress. This situation is especially challenging for English teachers, who must manage heavy workloads, including class preparation, grading, student behavior supervision, and extracurricular activities. Poor work environments, low morale, insufficient trust among staff, and inadequate communication further exacerbate this burnout. English teachers face the additional challenge of teaching students with varying language skill levels in the same classroom, often without proper training for addressing these differences. The consequences of burnout include lower engagement levels, declines in teaching quality, increased staff turnover, and lower job satisfaction. Consequently, these impacts extend beyond professional performance and affect teachers' physical and emotional well-being, thus ultimately compromising student learning outcomes. Methodology: The participants were selected based on Creswell's (2013) guidelines, which suggest that phenomenological research should typically involve a sample size of five to 25 participants. Six participants were selected through purposive sampling to ensure that the study included individuals who could provide relevant and valuable insights. Data was collected through phone interviews and Zoom meetings, allowing participants to share their experiences openly and flexibly. The data was analyzed using thematic analysis, which involved coding and interpreting the information provided by each participant. This process allowed researchers to identify repeated patterns, themes, and key points in the responses. Each participant's data was carefully reviewed and coded to capture the essence of their perspectives and experiences. Findings: The findings revealed that the key challenges contributing to English teacher burnout included student-related issues such as poor attitudes toward learning, disruptive behavior in class, and changes in student learning behavior after the COVID-19 pandemic. Other factors that exacerbated burnout include excessive workloads, lack of support for professional development, and inadequate teaching materials, especially for new teachers who were coping with multiple roles simultaneously. The no-fail grading policy in the Thai education system became another stressor for teachers, as it allows for redoing assignments and retests. This system lowered teacher teaching motivation and student learning commitment. Family responsibilities were another challenge for teachers, especially when balancing schoolwork and duties at home, as this significantly impacted their professional effectiveness. The study also highlighted the negative impacts of teacher burnout on teacher performance, as evidenced by reduced motivation to teach and a lack of class preparedness. This decline in teacher performance affects student learning, resulting in lower engagement in class. To cope with burnout, teachers opted for peer sharing and spiritual practices, such as prayer and worship group support, which emerged as essential pillars for addressing their emotional well-being. Contribution/Impact on Society: This study investigated the burnout experienced by Thai nationals who teach English in a faith-based school. Its findings highlighted the challenges that caused teacher burnout, and showed how they affect performance. Additionally, it revealed the support that teachers received. The study gave recommendations to teachers and schools regarding ways for teachers to prevent or reduce burnout and have good well-being in the workplace. Recommendations: Schools should promote policies focusing on teachers’ well-being to address burnout, such as seminars addressing teaching methods and student behavior. School administrators should consider reducing some teachers’ workloads and providing counseling services for students and teachers. In collaboration with other schools, efforts should be made to identify suitable and unsuitable textbooks, and to develop teaching materials so that teachers have the necessary resources for teaching. Research Limitations: This study had several limitations; teacher burnout is a sensitive topic that made participants uncomfortable sharing deep information about their school experiences. Another limitation was the translation of information from Thai to English. The interviews were conducted in Thai to ensure that participants could freely express themselves; however, some nuances might be lost during the translation process, which could affect interpretation of the data. Future Research: Future studies should use diverse and mixed research methods to understand burnout among English teachers. Additionally, research in Thai faith-based schools should focus on the unique experiences of teachers at various Christian schools. This approach may help to identify culturally relevant interventions to address burnout and improve teacher well-being. By exploring different perspectives and experiences, future studies may provide valuable insights that lead to more effective strategies and support systems for teachers in such settings.

Empowered learners are more motivated to perform classroom tasks, and they feel more competent in the classroom, find the required tasks more meaningful, and feel they have an impact on their learning process. Previous work has concluded that empowerment is primarily influenced by teacher behavior, which is not consistent with contemporary research on achievement motivation. The focus of the present study was to examine the role of student characteristics (temperament and learner orientation) on empowerment along with the impact of instructor communication behavior (nonverbal immediacy and clarity). Interpretation of results via the motivation model revealed teacher clarity to be the primary predictor of student empowerment and learning. Student temperament and learner orientation had little impact On empowerment.