Intrinsic Learning Research Articles

Online homework assignments: A case study for 5<sup>th</sup> grade of primary school in Greece

There is a growing body of scientific literature and research data recognizing the value of using online homework. Online homework may transform the way students learn outside the classroom, offering a dynamic and interactive approach to reinforcing learning objectives. This paper explores the concerns of educators arising from the integration of the online homework assignment and presents an educational intervention for 5<sup>th</sup> grade students. The aim of the intervention was to engage students in online homework as a supplementary material to the educational process. Results from the evaluation of the intervention are presented and the benefits of the intervention are highlighted. Although there are various technological, pedagogical and social challenges that need to be addressed, online homework can contribute to a more positive learning environment. Teachers who assign online homework seem to help enhance students’ intrinsic learning motivation so that students may achieve better learning outcomes.

Maximum entropy intrinsic learning for spiking networks towards embodied neuromorphic vision

Spiking neural network (SNN), as a brain-inspired model, possesses outstanding low power consumption and the ability to mimic biological neuron mechanisms. Embodied vision is a promising field, which requires the low-power advantage of SNNs. However, SNN faces difficulties in achieving real-time, high generalization ability, and robustness in the implementation of embodied vision due to the limitations of existing training methods. In this paper, to prevent model overfitting to noise and unknown environment in embodied neuromorphic visual intelligence, we present a new and efficient learning strategy designed to enhance the training performance of deep SNNs, called Spiking Maximum Entropy Intrinsic Learning (SMEIL). The learning algorithm essentially promotes the perturbation of the underlying source distribution, which in turn enlarges the predictive uncertainty of the current model. This approach enhances the model's robustness and improves its ability to generalize during the training process. Superior performance across a variety of data sets is achieved, and different types of noise are added to SMEIL algorithm for testing its robustness. Experiments show that SMEIL can consistently improve the learning robustness over each noise disturbance, and can cut down the power consumption during training significantly. Hence, it is a powerful method for advancing direct training of deep SNNs, and opens a novel point of view for developing novel spike-based learning algorithm towards embodied neuromorphic intelligence.

REVOLUTIONIZING POWER TRANSFORMER FAULT DIAGNOSIS THROUGH COGNITIVE ARTIFICIAL INTELLIGENCE AND DISSOLVED GAS ANALYSIS INTEGRATION

The research introduces a cognitive artificial intelligence (CAI) model that leverages dissolved gas analysis (DGA) to investigate power transformer faults. Conventional fault interpretation methods using DGA are limited in accuracy and uncertainty. In response, the proposed CAI model utilizes cognitive learning and direct interaction to achieve remarkably accurate fault identification without the need for supervised training. By extracting fault features through key gas ratio limitations. However, the CAI model also has a gap in data perception due to the information sensory challenges. Using gas ratios based on the conventional fault interpretation methods in the latest study still limited data perception of the CAI model to only three or four gas ratios. Thus, this study aims to increase data perception by extracting fault features through ten gas ratio limitations. The proposed CAI model's performance is validated, outperforming traditional methods like the Duval triangle method, Duval pentagon method, Doernenburg ratio method, and Roger ratio method, as well as common AI approaches including artificial neuron network, long short-term memory, nearest neighbor classifiers, support vector machine, ensemble classifiers, and decision trees. Notably, the CAI model's success rate in fault type identification stands at an impressive 98.04%. A distinctive trait of the CAI model is its autonomous knowledge accumulation and enhancement, enabled by inferring-fusion information and sensor-based knowledge integration. This intrinsic learning ability further contributes to its exceptional fault diagnosis accuracy. The proposed CAI model showcases promising potential for revolutionizing power transformer fault investigation and diagnosis, mitigating unplanned outages, and ultimately bolstering power system reliability.

The Influence of Augmented Reality Mobile App on Electronics Engineering Students' Self-Competence

Augmented reality (AR) is currently becoming educational trend by offering visualization of learning and making abstract concepts more concrete. Therefore, this research aimed to investigate the effect of AR technology based on mobile application on self-competence of electronics engineering students. It also considered the role of self-regulated learning, intrinsic learning motivation, and perceived usefulness as moderating factors. A cross-sectional survey with quantitative method was conducted on 848 electronics engineering education students at Universitas Negeri Padang, Indonesia. The data were obtained through questionnaires, and Structural Equation Model (SEM) analysis was conducted using SmartPLS 3. Furthermore, a fuzzy C-means clustering analysis was carried out using JASP software. The results showed that perceived usefulness had a significant positive effect on students' self-competence and moderated the significant positive relationship between self-regulated learning and self-competence. These empirical results showed that an improved level of self-regulated learning and beneficial application of technology could increase self-competence.

성취기준을 디지털화한 콘텐츠가 실과 내용 영역의 관심도에 미치는 영향

This study explored ways to evoke interest and enhance enthusiasm among university students in education learning the practical arts curriculum using text-based achievement standards, aiming to foster intrinsic learning motivation. The study employed an experimental approach with students from the College of Education, where content visually representing achievement standards for each practical arts subject area was developed and utilized for the learning process, with learner responses quantitatively analyzed. Data were collected through surveys, and changes in interest in achievement standards before and after applying the visual content were tested using bootstrapping and paired sample T-tests with a 95% confidence level. The analysis confirmed a significant increase in students' interest in practical arts content areas after exposure to the visualized content. Specifically, changes in interest before and after learning with digitized content of achievement standards showed significant differences in human development and proactive life, living environments and sustainable choices, sustainable technology and convergence, and digital society and artificial intelligence. Notably, interest in digital society and artificial intelligence increased the most, with high significance at the 1% level. In contrast, no significant difference was found in technological problem-solving or innovation. These results highlight the importance of using digital resources in teacher training programs to effectively teach achievement standards in practical arts education to university students. This, in turn, is expected to establish a robust foundation for future teachers to implement learner-centered education in actual teaching settings.

Channel-Blind Joint Source-Channel Coding for Wireless Image Transmission.

Joint source-channel coding (JSCC) based on deep learning has shown significant advancements in image transmission tasks. However, previous channel-adaptive JSCC methods often rely on the signal-to-noise ratio (SNR) of the current channel for encoding, which overlooks the neural network's self-adaptive capability across varying SNRs. This paper investigates the self-adaptive capability of deep learning-based JSCC models to dynamically changing channels and introduces a novel method named Channel-Blind JSCC (CBJSCC). CBJSCC leverages the intrinsic learning capability of neural networks to self-adapt to dynamic channels and diverse SNRs without relying on external SNR information. This approach is advantageous, as it is not affected by channel estimation errors and can be applied to one-to-many wireless communication scenarios. To enhance the performance of JSCC tasks, the CBJSCC model employs a specially designed encoder-decoder. Experimental results show that CBJSCC outperforms existing channel-adaptive JSCC methods that depend on SNR estimation and feedback, both in additive white Gaussian noise environments and under slow Rayleigh fading channel conditions. Through a comprehensive analysis of the model's performance, we further validate the robustness and adaptability of this strategy across different application scenarios, with the experimental results providing strong evidence to support this claim.

Examining classroom learning behaviors academic and athletic motivation in collegiate athletes

College student-athletes balance the demands of both sport and school in the context of higher education; high levels of motivation are necessary for success in both spaces. Studies have evaluated learning styles and academic motivational outcomes in college student athletes but there is a need to explore how motivation impacts both academic and athletic success. The present study examined the relationship academic, student athletic, and career athletic motivation had with learning styles and major (STEM vs. non-STEM) using Self-Determination Theory as the theoretical lens. College Division I, II, III, and Club college athletes took the Grasha-Reichmann Student Learning Style Scale and Student Athletes Motivation toward Sports and Academics Questionnaire. Learning styles were grouped into intrinsic (independent, participant, and collaborative) and extrinsic (dependent, avoidant, and competitive) styles. There were positive relationships between intrinsic learning style and both student athletic (r(147) = .19, p = .02) and academic (r(147) = .30, p < .001) motivation. Extrinsic learning style was positively correlated with career athletic (r(147) = .27, p = .001) and student athletic (r(147) = .16, p < .05) motivation but negatively corelated with academic motivation (r(147) = -.17, p = .03). Athletes in STEM majors (M = 3.88, SD = 0.36) had significantly higher academic motivation than non-STEM majors (M = 3.66, SD = 0.40), t(182) = 3.85, p < .001. Athletes in non-STEM majors (M = 4.00, SD = 0.88) had significantly higher career athletic motivation than STEM majors (M = 3.56, SD = 0.91), t(182) = -3.29, p = .001. Findings suggest being more independent, participant, and collaborative in class is related to motivation both in school and in student athletic endeavors. Athletes pursuing a STEM major may show higher academic motivation than athletes pursuing non-STEM majors. Encouraging student-athlete learning autonomy via education/intervention could improve intrinsic motivation in sport and classes, though further exploration of these factors is necessary to fully understand their relationship in college student-athletes.

Exploring the influence of instagram use on materialism and situational intrinsic learning motivation: An online experimental study

AbstractIn recent years, social media such as YouTube, TikTok, and Instagram have become an essential part of the everyday lives of children and young adults. Integrating elements of these social media into higher education may have the potential to enhance situational intrinsic learning motivation through the emotional design and proximity to students' lives, but this also poses the risk of fostering a situational materialistic focus due to the ubiquitous materialistic content on especially Instagram, undermining situational intrinsic learning motivation. In the present study, we examined if the primary use of Instagram is associated with higher materialism and how exposure to Instagram-framed pictures influences situational intrinsic learning motivation. The current study conducted an online experiment. Participants (N = 148) were randomly assigned to one of three groups after they rated items about general and problematic social media use and materialism. In the first two groups, participants were asked to rate the pleasantness of luxury or nature Instagram-framed pictures. A third group received no pictures. Afterwards, the situational intrinsic learning motivation was assessed through a mock working task. The findings prove that people who (primarily) use Instagram tend to be more materialistic than people who (primarily) use another social medium and that exposure to Instagram-framed pictures neither positively nor negatively influenced situational intrinsic learning motivation but moderated the relationship between problematic social media use and situational intrinsic learning motivation. Limitations, implications, and future directions for social media use inhigher education are discussed.

Antecedents of College Students’ Continuance Behaviors in Online Fragmented Learning: An Empirical Analysis from the Extended ECM Perspective

With the popularity of mobile networks and intelligent terminals, online fragmented learning, as a new learning method, has become the mainstream way for college students to acquire knowledge and study independently. However, college students are prone to “accept-interruption” in online fragmented learning; thus, it is difficult for them to master a complete knowledge system and form a rigorous logic system, which is essential to ensure the effect of online fragmented learning. Therefore, this study investigates the antecedents of college students’ continuance behaviors in online fragmented learning (CBOFL). Based on the expectation confirmation model (ECM), a theoretical model is developed to examine the factors influencing college students’ CBOFL. Taking a total of 429 undergraduate students who have studied contest courses on the Chinese university massive open online courses (MOOCs) for research subjects, the mechanism underlying the determinants of college students’ CBOFL is analyzed, and six hypotheses are tested by a structural equation modeling (SEM) technique with AMOS. The results indicate that confirmation positively impacts intrinsic learning motivation and satisfaction; intrinsic learning motivation, satisfaction, and teachers’ influence all significantly positively affect college students’ CBOFL. Additionally, the predicting powers of different factors on college students’ CBOFL vary broadly; therein, satisfaction has the most significant effect. This study makes theoretical contributions to the quantitative research on college students’ CBOFL and literature on the ECM. Still, it also has important practical significance in guiding college students’ CBOFL and facilitating the sustainability of online fragmented learning.

The impacts of learning motivation, emotional engagement and psychological capital on academic performance in a blended learning university course.

This study aims to explore the relationships among psychological capital, learning motivation, emotional engagement, and academic performance for college students in a blended learning environment. The research consists of two studies: Study 1 primarily focuses on validating, developing, revising, and analyzing the psychometric properties of the scale using factor analysis, while Study 2 employs structural equation modeling (SEM) to test the hypotheses of relationships of included variables and draw conclusions based on 745 data collected in a university in China. Findings revealed that intrinsic motivation, extrinsic motivation, emotional engagement, and psychological capital all impact academic performance. Extrinsic learning motivation has significant positive direct effects on intrinsic learning motivation, emotional engagement, and psychological capital. Intrinsic motivation mediates the relationship between extrinsic motivation and academic performance. In future blended learning practices, it is essential to cultivate students' intrinsic learning motivation while maintaining a certain level of external learning motivation. It is also crucial to stimulate and maintain students' emotional engagement, enhance their sense of identity and belonging, and recognize the role of psychological capital in learning to boost students' confidence, resilience, and positive emotions.

A comprehensive assessment of self-leadership among students in China’s ethnic regions: insights and implications for higher education

ABSTRACT In modern society, individuals are granted greater autonomy, amplifying the significance of self-leadership. The cultivation of self-leadership stands as a pivotal determinant in the holistic development of college students. Despite its importance, higher education’s role in fostering self-leadership has been comparatively overlooked, with empirical examinations using validated models in this domain remaining scarce. This research delves into the self-leadership attributes of students in Chinese ethnic region universities, utilizing the College Student Self-leadership Assessment (CSSA) and enriching the study with semi-structured interviews with students and educators. Drawing on responses from 2,578 participants across thirteen ethnic region universities in China, and insights gleaned from interviews, students’ self-leadership was evaluated across nine dimensions. The results illuminated that students generally exhibited commendable self-leadership attributes, particularly excelling in value selection and self-perception. Nevertheless, deficiencies were noted in their intrinsic learning motivation, goal management, and independent decision-making. Additionally, significant variances in self-leadership were detected across variables such as gender, grade and institutional tiers. These findings underscores the essential need for educational strategies that strengthen students’ self-leadership. Key recommendations involve adopting project-based learning that incorporates cultural engagement, and developing interdisciplinary curricula, which together enhance student autonomy and community involvement, thereby fostering a robust self-leadership foundation. This study provides key insights into pedagogical and assessment methods to develop self-leadership among students, filling a crucial gap in higher education research, especially in ethnic region universities, and underscores the need for specialized support for their developmental needs.

A Microlearning-Based Self-directed Learning Chatbot on Medication Administration for New Nurses: A Feasibility Study.

New nurses must acquire accurate knowledge of medication administration, as it directly affects patient safety. This study aimed to develop a microlearning-based self-directed learning chatbot on medication administration for novice nurses. Furthermore, the study had the objective of evaluating the chatbot feasibility. The chatbot covered two main topics: medication administration processes and drug-specific management, along with 21 subtopics. Fifty-eight newly hired nurses on standby were asked to use the chatbot over a 2-week period. Moreover, we evaluated the chatbot's feasibility through a survey that gauged changes in their confidence in medication administration knowledge, intrinsic learning motivation, satisfaction with the chatbot's learning content, and usability. After using the chatbot, participants' confidence in medication administration knowledge significantly improved in all topics ( P < .001) except "Understanding a concept of 5Right" ( P = .077). Their intrinsic learning motivation, satisfaction with the learning content, and usability scored above 5 out of 7 in all subdomains, except for pressure/tension (mean, 2.12; median, 1.90). They scored highest on ease of learning (mean, 6.69; median, 7.00). A microlearning-based chatbot can help new nurses improve their knowledge of medication administration through self-directed learning.

A study on the influence of learning space on students' intrinsic learning motivation

AbstractFollowing the continuous reform and innovation of classroom teaching methods and pedagogy in higher education, the support provided by traditional classrooms for new classes seems to be insufficient. Empirical exploration into the impact of learning spaces on student learning motivation, particularly in terms of how and to what extent, has been limited. This study focuses on student intrinsic learning motivation as the dependent variable and constructs an influence model incorporating factors such as learning space, pedagogy and classroom relationships. A comparative analysis is conducted to assess the differential influence between two learning space types: active learning spaces and traditional classrooms. Utilizing a convenience sampling method, students engaged in coursework at University A's ‘Future Learning Experience Center’ and traditional multimedia learning spaces over three academic terms were selected. Differential tests were performed on data collected from various learning environment types, and the impact of learning space, teaching methods and social relationships on students' intrinsic learning motivation was validated using structural equation modelling. Results indicate that scores in the dimensions of learning space, teacher–student relationships, student–student relationships and student intrinsic learning motivation were significantly higher in the active learning space compared to traditional classrooms. Surprisingly, learning space exhibited no significant direct effect on student intrinsic learning motivation but demonstrated a significant indirect effect. The influencing factors model presented in this study sheds light on the type of learning space's impact on student intrinsic learning motivation, offering theoretical guidance and practical data for future research endeavours.

A Study on the Change in Motivation for Physics Learning among Engineering Freshmen through General Physics Lectures Considering the Enhancement of Intrinsic Learning Motivation

A Study on the Change in Motivation for Physics Learning among Engineering Freshmen through General Physics Lectures Considering the Enhancement of Intrinsic Learning Motivation

Interplay of intrinsic motivation and well-being at school

Feeling well at school is crucial for both young people’s healthy self-development and their intrinsic learning motivation. Both concepts, subjective well-being and intrinsic motivation, are central to self-determination theory, but their reciprocal relationship is not studied very well. We hypothesized that the concepts affect each other directly: Students who are intrinsically motivated feel well at school due to positive emotions and the strive for understanding the learning content, which is an intrinsic motivation immanent. Beyond, students who feel well at school are intrinsically motivated, because subjective well-being represents an intrinsic value of the learning situation. This hypothesis is tested using a sample of 773 secondary school students (mean age: 12.7 years, SD = 1.8), who were answering questionnaires at the beginning (T1), at mid (T2) and at the end (T3) of a school year. The data were analyzed using a cross-lagged panel model. The results gave evidence on cross-lagged effects and an indirect effect from subjective well-being to intrinsic motivation to subjective well-being.

The Effect of Using Smart Application on Critical Literacy of Engineering Education Students

The incorporation of technology into educational settings is going through a period of rapid expansion, which is being accompanied by the persistent growth of a variety of forms of media. The purpose of this research is to investigate, among students majoring in engineering at Padang State University, how the utilization of learning apps contributes to the enhancement of fundamental reading and writing abilities. Learning Readiness was included as an independent variable alongside Intrinsic Learning Motivation and Critical Literacy, and Information Literacy was taken into consideration as a moderating variable. The present study involved the collection of a representative sample comprising 899 respondents. The collected data were subsequently subjected to analysis utilizing the Structural Equation Model (SEM). In this study we also conducted Fuzzy C-Means clustering, to see to what extent the value of each cluster can influence the others. The findings of this study indicate that individuals who have a high level of preparedness for learning and a strong sense of motivation are likely to achieve better outcomes. Additionally, possessing a proficient level of information literacy will exert an influence on the development of critical literacy.

GPT Modeling for English Learning Assistance Functions in Non-Native Language Environments

Abstract In this paper, we design an English multi-round dialogue model based on the pre-trained model GPT-2 and use Top-p and Top-k sampling instead of greedy search and cluster search sampling to optimize the decoding strategy. After that, a dialog history keyword replication mechanism is introduced to improve context consistency. Based on the GPT-2 English Multi-Round Dialogue Model, a conversational teaching model for teaching English in a non-native language environment has been constructed. The impact of this teaching model on students’ theoretical learning and teaching skills was examined. The results show that the BLEU1 index value of the GPT-2DH model after pre-training in the chatterbot corpus is 0.356, which is almost twice as much as that of seq2seq. As a result, the model in this paper is superior. Compared with traditional teaching methods, the GPT dialogic learning model significantly improved students’ theoretical learning performance, English listening and speaking skills, learning attitude (P=0.029&lt;0.05), and extrinsic learning motivation (P=0.046&lt;0.05) but had no significant effect on their instructional design skills and intrinsic learning motivation.

LEARNING SKILLS ACQUIRED AT SCHOOL FOR STEM STUDIES AT THE UNIVERSITY

In order to acquire science, it is necessary to perform hands-on activities, make experiments and scientific inquiry with the obtained data, to be able to evaluate critically and understand the scientific concepts, as well as to collaborate and communicate. STEM learning promotes inquiring minds, logical reasoning, and communication and collaboration skills. Studying science at the university, students need both hard – cognitive and subject specific skills and soft – interpersonal and intrapersonal skills. The aim of the study was to clarify the appropriateness/usefulness of the learning skills acquired at school for STEM studies at the university. The study consisted of two parts: a systematic review of Web of Science and SCOPUS databases and surveys of 242 first-year science students of the University of Latvia about the learning skills acquired at school performed in 2018–2020. The qualitative data processing program AQUAD was used for processing data obtained from students’ survey. Data coding was performed according to the code system developed specifically for this purpose. The qualitative data was transferred into a quantitative format, based on relations between students’ statements, and linkages among codes were created. The study allowed concluding that cognitive, interpersonal and intrapersonal skills acquired at school are intrinsic learning skills during the first study year in higher education. There are no principal differences in the evaluation of hard and soft skills in the systematic review and students’ responses that proves the appropriateness/usefulness of the 21st-century learning skills acquired at school for science studies at the university. Keywords: learning skills, STEM education, first-year university students

An Empirical Study on English Learning Motivation among Chinese Senior High School Students

&lt;p style="text-align: justify;"&gt;Motivation research in foreign language learning is booming, and self-determination theory (SDT) has attracted wide attention. This study used mixed methods with 410 Chinese senior high school students as participants. The study aimed to answer the following three main questions: (1) What are the levels of Chinese senior high school students’ English learning motivation (ELM)? (2) Do the students’ ELM levels differ by gender? (3) What are the influencing factors of the students’ ELM? The following conclusions were drawn from the data analysis. First, the participants had a median level of ELM. Second, their ELM was more inclined towards self-determination. Third, girls’ levels of intrinsic regulation and identified regulation were significantly higher than those of boys. Fourth, anxiety and negative peer influence were two factors that contributed to a decline the students’ ELM. Cooperative learning and teacher support could improve students’ intrinsic learning motivation. The satisfaction of these three basic psychological needs promoted the internalization of external motivation. Based on the above findings, we offer the three following insights: it is crucial to maintain students’ autonomous ELM level; it is imperative to create a relaxed and positive classroom atmosphere; and teachers should try to meet students’ basic psychological needs.&lt;/p&gt;

Normative tDCS over V5 and FEF reveals practice-induced modulation of extraretinal smooth pursuit mechanisms, but no specific stimulation effect

The neural networks subserving smooth pursuit eye movements (SPEM) provide an ideal model for investigating the interaction of sensory processing and motor control during ongoing movements. To better understand core plasticity aspects of sensorimotor processing for SPEM, normative sham, anodal or cathodal transcranial direct current stimulation (tDCS) was applied over visual area V5 and frontal eye fields (FEF) in sixty healthy participants. The identical within-subject paradigm was used to assess SPEM modulations by practice. While no specific tDCS effects were revealed, within- and between-session practice effects indicate plasticity of top-down extraretinal mechanisms that mainly affect SPEM in the absence of visual input and during SPEM initiation. To explore the potential of tDCS effects, individual electric field simulations were computed based on calibrated finite element head models and individual functional localization of V5 and FEF location (using functional MRI) and orientation (using combined EEG/MEG) was conducted. Simulations revealed only limited electric field target intensities induced by the applied normative tDCS montages but indicate the potential efficacy of personalized tDCS for the modulation of SPEM. In sum, results indicate the potential susceptibility of extraretinal SPEM control to targeted external neuromodulation (e.g., personalized tDCS) and intrinsic learning protocols.