Development of an AI predictive model to categorize and predict online learning behaviors of students in Thailand.

This qualitative study has three objectives: (1) to develop a predictive AI model to categorize the online learning behavior of Thai students who study through a Thai Massive Open Online Course (MOOC); (2) to categorize students' online behavior in a Thai MOOC; and (3) to evaluate the prediction accuracy of the developed predictive AI models. Data were collected from 8000 learners enrolled in the KMUTT015 course on the Thai MOOC platform. The k-means clustering algorithm classified learners enrolled in the Thai MOOC platform based on their online learning behaviors. The decision tree algorithm was used to assess the accuracy of the AI model prediction capability. The study finds the predictive AI model successfully categorizes learners based on their learning behaviors and predicts their future online learning behaviors in the online learning environment. The k-means clustering algorithm yields three groups of learners in the Thai MOOC platform: High Active Participants (HAP), Medium Active Participants (MAP), and Lurking participants. The findings also indicate high predictive accuracy rates for each behavioral group (HAP cluster = 0.98475, Lurking participants cluster = 0.967625, and MAP cluster = 0.955375), indicating the proficiency of the AI predictive model in forecasting learner behavior. The results of this study will benefit the design of online courses that respond to the needs of students with different online learning characteristics and help them achieve a high level of academic performance.

Unraveling motives: identifying the impact of university attendance motives on learning behaviors among dental students.

PURPOSE: Students report various motives for attending university (MAU) grouped under five categories, namely, personal-intellectual development (PER), humanitarian (HUM), careerist-materialist (CAR), expectation-driven (EXP), and uncertain motives. Although the literature demonstrates that these motives exert an influence on learning and achievement, relatively less attention is given to this issue in the context of dental students. This study aimed to examine the relationship among the mindsets, MAU, academic engagement (AE), and DAL of dental students and to test the mediating effect of AE on the relationship between MAU and deep approach to learning (DAL). METHODS: The study recruited 226 dental students at various levels of the curriculum, who responded to four questionnaires for measuring MAU, DAL, mindsets, and AE. The study employed structural equation modeling to analyze the mediation effects of AE on the relationship between MAU and DAL and to determine the influence of mindsets on MAU. RESULTS: This model reveals the significant relationships of a growth mindset with CAR, PER, and HUM. Moreover, the study finds that a fixed mindset was associated with CAR, EXP, and uncertain motives. Furthermore, AE only fully mediated the significant positive relationship between PER and DAL, whereas CAR negatively predicted DAL without a mediator. CONCLUSIONS: These findings suggest that administering the inventories in a dental school setting can facilitate a more comprehensive understanding of students' mindsets toward learning and effective processes related to learning. This understanding can inform instructors' pedagogical practices, enabling them to provide more effective guidance to students navigating the complexities of academic coursework.

Improved Learning Gain in Medical Students by Using Animated Whiteboard-Videos in Comparison to Textbooks in Surgery.

BACKGROUND: Animated videos have become popular in teaching medical students, although there is a certain lack of evidence concerning its efficacy. Surgery seems to be an ideal field for its application, since animations are very helpful to understand anatomic structures and complex procedures. The aim of this study was to investigate the effects of animated videos compared to textbooks on learning gain. METHODS: A prospective 2-arm cohort study with 5th-year medical students was conducted during their 2-week surgical training module. The initial cohort of students received textbook sections on 3 major topics in visceral surgery as learning medium (text cohort). During the following semester, the second cohort of students received 3 animated whiteboard videos (animated videos) containing equivalent content (video cohort). All participants completed a multiple-choice test consisting of 15 questions on the learning content at baseline (pre-test) and after the learning period (post-test) and answered an additional evaluation questionnaire. RESULTS: Both cohorts were similar in their descriptive data and demonstrated significant learning gain during the 2-week learning period. The video cohort achieved better results (80% vs 73% correct answers; P = .028) and a higher learning gain (17% vs 11%; P = .034) in the post-test compared to the text cohort. The estimated learning time was longer in the video cohort (62 min vs 37 min; P < .001) and watching the videos resulted in higher learning gain (21% vs 6%; P < .001). Subgroups with higher learning gain by video learning were female gender (20% vs 11%; P = .040), native German speakers (18% vs 11%; P = .009), students without prior surgical experience (19% vs 12%; P = .033) and those undecided concerning a surgical career (22% vs 9%; P = .020). Interestingly, "low digital orientation" students benefited from videos (22% vs 13%; P = .021), whereas "high digital orientation" students did not. CONCLUSIONS: Animated videos increase medical students' learning gain and interest in surgery.

How do grazing beef and dairy cattle respond to virtual fences? A review.

Virtual fencing (VF) is a modern fencing technology that requires the animal to wear a device (e.g., a collar) that emits acoustic signals to replace the visual cue of traditional physical fences (PF) and, if necessary, mild electric signals. The use of devices that provide electric signals leads to concerns regarding the welfare of virtually fenced animals. The objective of this review is to give an overview of the current state of VF research into the welfare and learning behavior of cattle. Therefore, a systematic literature search was conducted using two online databases and reference lists of relevant articles. Studies included were peer-reviewed and written in English, used beef or dairy cattle, and tested neck-mounted VF devices. Further inclusion criteria were a combination of audio and electrical signals and a setup as a pasture trial, which implied that animals grazed in groups on grassland for 4 h minimum while at least one fence side was virtually fenced. The eligible studies (n = 13) were assigned to one or two of the following categories: animal welfare (n studies = 8) or learning behavior (n studies = 9). As data availability for conducting a meta-analysis was not sufficient, a comparison of the means of welfare indicators (daily weight gain, daily lying time, steps per hour, daily number of lying bouts, and fecal cortisol metabolites [FCM]) for virtually and physically fenced animals was done instead. In an additional qualitative approach, the results from the welfare-related studies were assembled and discussed. For the learning behavior, the number of acoustic and electric signals and their ratio were used in a linear regression model with duration in days as a numeric predictor to assess the learning trends over time. There were no significant differences between VF and PF for most welfare indicators (except FCM with lower values for VF; P = 0.0165). The duration in days did not have a significant effect on the number of acoustic and electric signals. However, a significant effect of trial duration on the ratio of electric-to-acoustic signals (P = 0.0014) could be detected, resulting in a decreasing trend of the ratio over time, which suggests successful learning. Overall, we conclude that the VF research done so far is promising but is not yet sufficient to ensure that the technology could not have impacts on the welfare of certain cattle types. More research is necessary to investigate especially possible long-term effects of VF.

Virtual fencing is a GPS-enabled fencing technology with the potential for improved livestock and pasture management, as well as socioeconomic and environmental benefits. However, the missing visual cue of a physical fence and the use of electric signals to ensure animals stay within the invisible boundary raise ethical and animal welfare concerns regarding the animal's ability to understand and learn the technology and the stress and anxiety associated with these processes. In this review, data from studies investigating the welfare and learning behaviors of virtually fenced animals were collected and analyzed to give an overview of this research field. It shows that the welfare of cattle in extensive systems is not adversely affected by the virtual fencing system, and the animals learn to avoid the electric signals. However, more research is necessary, especially over longer periods of time and with cows in intensive grazing systems, to ensure the welfare of virtually fenced cattle.

Stress indicators in dairy cows adapting to virtual fencing.

Virtual fencing (VF) enables livestock grazing without physical fences by conditioning animals to a virtual boundary delimited with an audio tone (AT) and an electric pulse (EP). The present study followed the adaptation process of lactating dairy cows to a VF system with changing virtual boundaries and investigated its impact on animal welfare. Twenty cows were divided into stratified groups (2× VF; 2× electric fencing, EF) of five individuals. Each group grazed half-days in a separate EF paddock of comparable size during 3 d of acclimation (P0), followed by 21, 14, 14, and 7 d of experimental treatment (P1 to P4). At the start of the trial, all cows were equipped with an IceQube pedometer (Peacock Technology Ltd, Stirling, UK) and a VF collar (Nofence AS, Batnfjordsøra, Norway). During P0, cows were accustomed to their first paddock with a deactivated virtual boundary and wearing the sensors. In P1 to P4, an active virtual boundary for the VF groups, and a second EF for the EF groups was set up parallel to an outer EF within their paddock. Throughout the trial, the sensors continuously tracked cow positions and activity behavior at 15-min intervals. From P1 onwards, the VF collars additionally recorded each AT and EP per cow with a georeferenced time stamp. During P0 to P4, daily feed intake, body weight, and milk yield were recorded in the barn. A total of 26 milk samples were collected per cow to determine milk cortisol levels. Behavioral observations were conducted for 2 h on day 23 to record agonistic behaviors, vocalizations, and excretions. The total number of stimuli per cow ranged from 37 to 225 ATs (mean ±â€…SD: 1.9 ±â€…3.3 per day) and 3 to 11 EPs (mean ±â€…SD: 0.1 ±â€…0.7 per day) throughout the trial. The maximum number of EPs per day was 8 for an individual cow and occurred once on D1. Mean EP/AT decreased by 55% during the first three half-days of grazing and with each paddock change from 0.2 EP/AT in week 1 to 0.03, 0.02, and 0 EP/AT in weeks 4, 6, and 8, respectively. Linear and generalized mixed effects models revealed that milk yield and cortisol, feed intake, body weight, and activity and lying behavior did not significantly differ between VF and EF groups. A higher number of agonistic behaviors were observed in the VF groups when the VF system was activated. However, due to the short observation periods only few contacts were observed in total. Overall, all cows adapted to the VF system without evidence of lasting adverse effects on animal welfare.

Virtual fences are commercially available but face restrictions in some countries due to animal welfare concerns. For virtual fencing (VF), animals are equipped with collars that emit audio tones (ATs) followed by electric pulses (EPs) when they cross a virtual boundary tracked by global navigation. Existing studies have so far not covered the aspect of longer-term learning, impacting possibly VF suitability. The present study followed therefore the learning process of dairy cows with changing virtual boundaries and examined behavior and stress indicators in dairy cows during an 8-wk adaptation to VF across four experimental periods. Four control and treatment groups of five cows each were investigated. EPs occurred most frequently on days 1 to 3 and remained low for the remaining experiment. In the latter two experimental periods, almost no EPs were recorded while ATs were still triggered, indicating that it took the animals two introductions to a new fence line to respond to the ATs only. Animal welfare was assessed by monitoring cow activity and lying behavior, milk yield, milk cortisol, feed intake, body weight, and frequencies of agonistic interactions, vocalizations, and excretions. All cows adapted to the VF system without compromising animal welfare during the study period.

Erratum: Understanding the role of peer pressure on engineering students' learning behavior: a TPB perspective.

[This corrects the article DOI: 10.3389/fpubh.2022.1069384.].

Student Learning Behavior Recognition Incorporating Data Augmentation with Learning Feature Representation in Smart Classrooms.

A robust and scientifically grounded teaching evaluation system holds significant importance in modern education, serving as a crucial metric that reflects the quality of classroom instruction. However, current methodologies within smart classroom environments have distinct limitations. These include accommodating a substantial student population, grappling with object detection challenges due to obstructions, and encountering accuracy issues in recognition stemming from varying observation angles. To address these limitations, this paper proposes an innovative data augmentation approach designed to detect distinct student behaviors by leveraging focused behavioral attributes. The primary objective is to alleviate the pedagogical workload. The process begins with assembling a concise dataset tailored for discerning student learning behaviors, followed by the application of data augmentation techniques to significantly expand its size. Additionally, the architectural prowess of the Extended-efficient Layer Aggregation Networks (E-ELAN) is harnessed to effectively extract a diverse array of learning behavior features. Of particular note is the integration of the Channel-wise Attention Module (CBAM) focal mechanism into the feature detection network. This integration plays a pivotal role, enhancing the network's ability to detect key cues relevant to student learning behaviors and thereby heightening feature identification precision. The culmination of this methodological journey involves the classification of the extracted features through a dual-pronged conduit: the Feature Pyramid Network (FPN) and the Path Aggregation Network (PAN). Empirical evidence vividly demonstrates the potency of the proposed methodology, yielding a mean average precision (mAP) of 96.7%. This achievement surpasses comparable methodologies by a substantial margin of at least 11.9%, conclusively highlighting the method's superior recognition capabilities. This research has an important impact on the field of teaching evaluation system, which helps to reduce the burden of educators on the one hand, and makes teaching evaluation more objective and accurate on the other hand.

A PDMS-Agar Hybrid Microfluidic Device for the Investigation of Chemical-Mechanical Associative Learning Behavior of C. elegans.

Associative learning is a critical survival trait that promotes behavioral plasticity in response to changing environments. Chemosensation and mechanosensation are important sensory modalities that enable animals to gather information about their internal state and external environment. However, there is a limited amount of research on these two modalities. In this paper, a novel PDMS-agar hybrid microfluidic device is proposed for training and analyzing chemical-mechanical associative learning behavior in the nematode Caenorhabditis elegans. The microfluidic device consisted of a bottom agar gel layer and an upper PDMS layer. A chemical concentration gradient was generated on the agar gel layer, and the PDMS layer served to mimic mechanical stimuli. Based on this platform, C. elegans can perform chemical-mechanical associative learning behavior after training. Our findings indicated that the aversive component of training is the primary driver of the observed associative learning behavior. In addition, the results indicated that the neurotransmitter octopamine is involved in regulating this associative learning behavior via the SER-6 receptor. Thus, the microfluidic device provides a highly efficient platform for studying the associative learning behavior of C. elegans, and it may be applied in mutant screening and drug testing.

Analysis of participation and performance of MOOC learners via latent class analysis: A retrospective study based on the data of a nursing MOOC from 2018 to 2022.

BACKGROUND: Although massive open online courses have been widely used in nurse education, few studies have evaluated MOOC learner behavioral characteristics. Understanding MOOC learners' participation and performance parameters is helpful for further development and administration of this educational approach. OBJECTIVES: To categorize nursing MOOC learners according to their different learning participation and to compare the differences in learning performance of different types of MOOC learners. DESIGN: Retrospective. SETTINGS AND PARTICIPANTS: Participants evaluated in this study were learners of the Health Assessment MOOC on a Chinese MOOC platform for nine semesters from 2018 to 2022. METHODS: Via latent class analysis, MOOC learners were categorized according to the number of times they participated in each topic test and the final exam. Differences in scores of each topic test and the final exam, case discussion number, and total evaluation score among different learners were compared. RESULTS: Using latent class analysis, MOOC learners were categorized as committed (28.96 %), negative (16.08 %), mid-term dropout (12.78 %) and early dropout (42.18 %) learners. Committed learners performed best and no significant difference were found among other learner types on most topic tests and the final exam. Committed learners participated in case discussions most actively. According to total evaluations, committed, mid-term dropout, early dropout, and negative learners performed from best to worst. CONCLUSION: Health Assessment MOOC learners were categorized using five-years of data. Committed learners performed best. No significant difference in performance was found for other learners on most topic tests and the final exam. Understanding learner characteristics and educational behavior is critical for effective design and administration of future MOOC learning approaches.

Development of the Cultural Tightness-Looseness Orientation Scale for College Students.

Background: "The cultural tightness-looseness orientation of college students", which involves college students' cognition about tolerance for non-learning-behavior in class, strength of learning-behavior norms in class, and strength of social norms in the generalized macro-context, offers a new perspective to explain college students' psychology and behavior and could effectively promote their all-round development. However, there is severely lack of a reliable and valid instrument. Hence, we seek to develop the Cultural Tightness-Looseness Orientation Scale for College Students (CTLOS-S) in the Chinese context. Methods: We firstly pooled the initial 17 measuring items of CTLOS-S through literature review and the open-ended interview. After conducting questionnaire survey among 264 college students using the initial scale, we did a series of reliability and validity tests to get the formal CTLOS-S, based on which we further administered questionnaire survey among 755 college students to check its reliability, construct validity, criterion validity, content validity, and across-gender invariance. Results: The formal CTLOS-S contains 7-item subscale of tolerance orientation for non-learning-behavior in class, 4-item subscale of strength orientation of learning-behavior norms in class, and 3-item subscale of strength orientation of social norms in the generalized macro-context. The testing results of the second-stage questionnaire survey data (N = 755) demonstrate that the reliability coefficients of CTLOS-S and its three subscales are 0.85, 0.85, 0.83, and 0.76 separately, the internal 3-factor structure validity of CTLOS-S is satisfactorily acceptable with χ 2(74) = 318.76, CFI = 0.94, TLI = 0.93, RMSEA = 0.06, and SRMR = 0.04, and the content validity and criterion validity are satisfactory as the total score of CTLOS-S is positively correlated with each score of its three subscales and the total score of learning engagement scale. Besides, the 3-factor structure of CTLOS-S is invariant across gender. Conclusion: The 14-item CTLOS-S we develop is a reliable and valid instrument for researchers to conduct quantitative studies on college students' cultural tightness-looseness orientation.

Learners' characteristics and the mastery of digital education during the COVID-19 pandemic in students of a medical faculty in Germany.

BACKGROUND: In response to the spread of the coronavirus, educational institutions have been closed and digital education has become a new teaching method to ensure the continuity of medical education. Since this format was a new form of learning for students at medical faculties in Germany, little is known about the perception of it and the factors that contribute to successful mastery. The current study aimed to analyze students' learning experiences during the first online semester and to identify associations between learners' characteristics and enjoyment, mastery experiences, as well as the perceived stress level. METHODS: In this cross-sectional study, students of a medical faculty from Germany answered an online questionnaire including information about perceptions towards digital education and learners' characteristics (study skills and dispositions). Data were analyzed using multivariate linear regression analysis. RESULTS: In total, 383 students responded to the online survey. A majority of students felt at least somewhat worse about their studies compared to before the pandemic. Success of study tasks was related to preferences for cooperative learning (B = - 0.063, p < .001) and success of study organization was associated to the use of metacognitive learning strategies (B = 0.019, p = .04). Enjoyment of studying in times of digital education was positively related to the use of metacognitive strategies (B = 0.049, p = .04) and self-efficacy (B = 0.111, p = .02). The perceived stress was influenced by cognitive strategies (B = 0.401, p = .02) and test anxiety (B = 0.466, p < .001). CONCLUSIONS: Although students perceive digital teaching as a good alternative for big courses, those with low self-efficacy beliefs and low self-regulation have problems in coping with the demands of this learning format and need further support.

Psychological improvement in Employee Productivity by Maintaining Attendance System using Machine Learning Behavior.

For a very long time, researchers, educationists, practitioners, and psychologists have tried to conduct extensive research on employee productivity at the workplace. It was firmly believed that positive traits of the employees positively affect the performance of employees at the workplace and, at the same time, increase the productivity of the organization. A few organizations have changed their organizational policies to improve employee performance by identifying individuals' positive traits. Positive psychology (PS), unlike traditional psychology, focuses on the positive traits of the individuals rather than on their negative aspects. Hence, this paper intends to analyze the important role of PS in improving employee productivity as well as analyze the psychological improvement in Employee Productivity by Maintaining Attendance Systems, Implementing Corporate Social Responsibility (CSR activities), Entrepreneurial Intentions (EI), and Machine Learning Behavior (MLB). This study paper investigates the role of attendance systems, CSR, EI, and MLB in enhancing the employees' productivity in a positive manner, which will eventually increase organizational productivity. This study proposes a research framework and hypothesis that explains the relationship between employee productivity and organizational attendance system, CSR, EI, and MLB. For this study, a questionnaire was prepared and distributed among the employees working in the selected organizations. These data verify the validity of the proposed hypothesis and research framework. Statistical approaches were employed to analyze the results of the proposed hypothesis. Results of the study found that by maintaining an attendance system, implementing CSR, EI, and MLB, employee performance can be improved positively, thereby increasing organizational productivity.

Comparison of students' self-evaluated learning behavior and achievement between online and offline problem-based learning / 中华医学教育探索杂志

Objective:To compare the self-evaluated learning behavior and achievement of eight-year-program clinical medical students on online and offline problem-based learning (PBL), and to provide references and suggestions for successful implementation of online PBL teaching.Methods:During the three semesters of September 2020 to January 2022, a survey was performed on 100 clinical medical students of 2018 eight-year program on online or offline PBL teaching in Xiangya School of Medicine, Central South University. At the end of PBL courses, the students received questionnaires to self-assess their learning behavior and achievement in online or offline PBL teaching. The data were analyzed using the paired rank sum test with the use of SPSS 26.0.Results:Before class, there were no significant differences in learning behaviors including preparation time, communication with classmates, and materials collecting and sorting between students on online and offline PBL teaching ( P>0.05). In class, no significant differences were found in students' initiative of speaking and degree of satisfaction with classmates' speaking between online and offline teaching ( P>0.05), but the students on offline teaching showed significantly better concentration of attention ( Z=-4.24, P<0.001), listening to classmates' speeches ( Z=-3.42, P=0.001), and classmates' interaction ( Z=-3.11, P=0.002) compared with those on online teaching. No significant differences were observed between online and offline PBL teaching in students' self-evaluated learning achievements including improvements in autonomous learning ability, problem-solving ability, and literature inquiry skills ( P>0.05). Conclusion:Students on online PBL teaching have generally as good pre-class preparation and in-class learning performance as those on offline PBL teaching, indicating that the online PBL mode can be well implemented, and it can also ensure almost the same learning achievement as offline teaching.

Learning behavior, digital platforms for learning and its impact on university student's motivations and knowledge development.

Background: Learning digital technologies in higher education is a process of knowledge generation, and the rapid growth of technology in education has a significant impact on students' learning behaviors, motivation, and knowledge development. Pakistan's remarkable technological breakthrough has increased in the education field. Study objectives: The study focuses on estimating students' learning behaviors, identifying the positive influence of educational apps on digital learning platforms, and analyzing their impact on students' motivation and knowledge development. Materials and methods: According to the study's objectives, a questionnaire survey was conducted to gather the primary data. The participants were students of universities in Lahore city of Pakistan. For this study, the sample size was N = 300, carefully chosen using the purposive sampling technique. Of the respondents, there were 146 male and 154 female students, and the sample consisted of individuals aged 25-35 years. Smart-PLS-Bootstrapping, T-Values (PLS) 3.2.9 and the structural equation model (SEM) were applied to get the appropriate outcomes from the proposed study framework. Results: SEM analysis results shows that all proposed hypotheses [Animated Movies (AM) -> Student Motivation (SM), Educational Apps (EA) -> Knowledge Development (KD), Learning Behavior (LB) -> Animated Movies, Learning Behavior -> Educational Apps, Learning Behavior -> Knowledge Development, Learning Behavior -> Virtual Classrooms (VCr), Virtual Classrooms -> Knowledge Development, Virtual Classrooms -> Student Motivation] are confirmed while Learning Behavior -> Student Motivation is not confirmed. Conclusion: This study found that digital learning platforms significantly impact students' learning and what motivates them to learn. The study also found that using educational apps and virtual classrooms more often helps students learn more and be more motivated to learn.

Task-selective place cells show behaviorally driven dynamics during learning and stability during memory recall.

Decades of work propose that hippocampal activity supports internal representation of learned experiences and contexts, allowing individuals to form long-term memories and quickly adapt behavior to changing environments. However, recent studies insinuate hippocampal representations can drift over time, raising the question: how could the hippocampus hold stable memories when activity of its neuronal maps fluctuates? We hypothesized that task-dependent hippocampal maps set by learning rules and structured attention stabilize as a function of behavioral performance. To test this, we imaged hippocampal CA1 pyramidal neurons during learning and memory recall phases of a new task where mice use odor cues to navigate between two reward zones. Across learning, both orthogonal and overlapping task-dependent place maps form rapidly, discriminating trial context with strong correlation to behavioral performance. Once formed, task-selective place maps show increased long-term stability during memory recall phases. We conclude that memory demand and attention stabilize hippocampal activity to maintain contextually rich spatial representations.

SA-FEM: Combined Feature Selection and Feature Fusion for Students' Performance Prediction.

Around the world, the COVID-19 pandemic has created significant obstacles for education, driving people to discover workarounds to maintain education. Because of the excellent benefit of cheap-cost information distribution brought about by the advent of the Internet, some offline instructional activity started to go online in an effort to stop the spread of the disease. How to guarantee the quality of teaching and promote the steady progress of education has become more and more important. Currently, one of the ways to guarantee the quality of online learning is to use independent online learning behavior data to build learning performance predictors, which can provide real-time monitoring and feedback during the learning process. This method, however, ignores the internal correlation between e-learning behaviors. In contrast, the e-learning behavior classification model (EBC model) can reflect the internal correlation between learning behaviors. Therefore, this study proposes an online learning performance prediction model, SA-FEM, based on adaptive feature fusion and feature selection. The proposed method utilizes the relationship among features and fuses features according to the category that achieved better performance. Through the analysis of experimental results, the feature space mined by the fine-grained differential evolution algorithm and the adaptive fusion of features combined with the differential evolution algorithm can better support online learning performance prediction, and it is also verified that the adaptive feature fusion strategy based on the EBC model proposed in this paper outperforms the benchmark method.

Cell phone use distracts young adults from academic work with limited benefit to self-regulatory behavior.

We aim to uncover theoretical mechanisms associated with potential negative (i.e., multitasking) and positive (i.e., self-regulation) aspects of cell phone use (CPU) for academic performance in young adults. We hypothesized that, according to the Switch-Load Theory, repeated CPU during academic activities (CPU_Multitasking) would relate negatively, whereas, according to Zimmerman's Theory of Self-Regulated Learning, CPU for self-regulated learning behaviors (CPU_SRLBehavior) would relate positively to the academic performance of undergraduate students. 525 (75.4% female) undergraduate students from a large public university participated in this study during fall 2019 by completing validated quantitative surveys accessing their CPU and academic performance. Spearman's rho was used to compute the correlations and hierarchical regression was used to analyze the variance. Spearman rank-order coefficient showed that CPU_Multitasking relates negatively, but CPU_SRLBehavior is unrelated to the college GPA of undergraduate students. Hierarchical regression showed that CPU_Multitasking was not a significant predictor of academic performance. Young adults who switch to their cell phones during class or study-related activities are more likely to have lower performance in exams as CPU_Multitasking costs time and efficiency (Switch Load Theory). Young adults who use their cell phones for self-regulated learning behavior are less likely to have an impact on their academic performance as CPU_SRLBehavior helps regulate habits but not learning processes. With the known theoretical mechanisms for CPU multitasking and SRL Behavior, this study provides a guiding document for educational computing system practitioners to explore more theory-driven empirical approaches in the field of CPU and academic success.

Will Abusive Supervision Promote Subordinates' Voluntary Learning Behavior?

Abusive supervision was traditionally viewed as a unidimensional construct and found detrimental in various fields, while there may be subdimensions associating with different consequences. This study aims to justify two subdimensions of abusive supervision, namely overt abusive supervision and covert abusive supervision, and investigate their effects on subordinates' voluntary learning behavior, with public self-consciousness as a moderator. Data was acquired from a sample of 443 employees from China through a two-wave survey, and hypotheses were tested by hierarchical regression analysis. The empirical results demonstrated that overt abusive supervision promotes subordinates' voluntary learning behavior at lower levels of public self-consciousness and hinders it otherwise, while covert abusive supervision promotes subordinates' voluntary learning behavior homogeneously at different levels of public self-consciousness. The results suggest that supervisors could be mean and critical when encouraging subordinates to improve themselves, with subordinates' public self-consciousness taken into consideration. However, abusive supervision should never be overused, not only because it is unethical and detrimental in many other fields, but also because the abused subordinates may just be preparing for leaving.

Differential Antecedents and Consequences of Affective and Cognitive Ruminations.

Adopting the information processing perspective, the current study aims to investigate the differential effects of affective and cognitive ruminations on individuals' affective states and learning behavior, and to further explore their differential mediating roles in transmitting effects of challenge and hindrance stressors on affect and behavior. A two-wave survey, in which stressors and ruminations were measured in the first wave and affective states and learning behavior were measured in the second, was conducted to obtain responses from 410 employees. As expected, affective and cognitive ruminations were differently associated with challenge stressors (i.e., cognitive job insecurity) and hindrance stressors (i.e., interpersonal conflict), and yielded different effects in terms of positive affect, negative affect, and learning behavior. Specifically, the results showed that: (a) cognitive job insecurity was significantly and positively related to cognitive rumination, while interpersonal conflict was significantly and positively related to affective rumination; (b) affective and cognitive ruminations were significantly associated with positive or negative affect, but in the exact opposite direction; (c) cognitive rumination, but not affective rumination, was significantly related to learning behavior; and (d) cognitive rumination mediated the effect of cognitive job insecurity on positive affect and learning behavior, while affective rumination mediated the effect of interpersonal conflict on negative affect. The current study contributes to the literature on rumination by introducing a new perspective, and sheds new light on the understanding of how and why affective and cognitive ruminations may lead to different affective states and behaviors.

Understanding Motivation, Career Planning, and Socio-Cultural Adaptation Difficulties as Determinants of Higher Education Institution Choice Decision by International Students in the Post-pandemic Era.

The world is facing an unprecedented health crisis with the spread of COVID-19 across different corners of the globe. This pandemic has raised more significant concerns about international students' learning environment, personality development, and career planning, particularly in high-ranked institutes in China. Now the question concerning this dilemma is, would the COVID-19 pandemic negatively affect students' education and the country culture where they are bound to seek information and the subject education? Therefore, this study examines the impact of innovative learning environment, career planning, and socio-cultural adaptation-related difficulties faced by international students as determinants of higher education institution choice decisions made by international students in the post-pandemic era. This quantitative study examined international students in high-ranked universities across China. The data from 260 students were collected through a structured questionnaire and analyzed using the AMOS technique. Moreover, it has been observed that the current global health crisis has intensified social inequalities across different international higher education systems. Countries fail to maintain the scale of the innovative international learning environment. The results further indicated that international students are more considerate of innovative learning environments, cultural adoption, career planning, and personality development, specifically after the outbreak of the COVID-19 pandemic, which has drastically affected the global higher education system. Unusually, more than half of the participants wanted to maintain the option of overall distance education after the pandemic. However, apart from this argument, it is appropriate to demand significant changes in post-pandemic education adapted to the post-digital era and to satisfy the concerns and expectations of the students.