Learning Progress Research Articles

Surgical Residents' Perception of Feedback on Their Education: Protocol for a Scoping Review.

Feedback is an essential tool for learning and improving performance in any sphere of education, including training of resident physicians. The learner's perception of the feedback they receive is extremely relevant to their learning progress, which must aim at providing qualified care for patients. Studies pertinent to the matter differ substantially with respect to methodology, population, context, and objective, which makes it even more difficult to achieve a clear understanding of the topic. A scoping review on this theme will unequivocally enhance and organize what is already known. The aim of this study is to identify and map out data from studies that report surgical residents' perception of the feedback received during their education. The review will consider studies on the feedback perception of resident physicians of any surgical specialty and age group, attending any year of residency, regardless of the type of feedback given and the way the perceptions were measured. Primary studies published in English, Spanish, and Portuguese since 2017 will be considered. The search will be carried out in 6 databases and reference lists will also be searched for additional studies. Duplicates will be removed, and 2 independent reviewers will screen the selected studies' titles, abstracts, and full texts. Data extraction will be performed through a tool developed by the researchers. Descriptive statistics and qualitative analysis (content analysis) will be used to analyze the data. A summary of the results will be presented in the form of diagrams, narratives, and tables. The findings of this scoping review were submitted to an indexed journal in July 2024, currently awaiting reviewer approval. The search was executed on March 15, 2024, and resulted in 588 articles. After the exclusion of the duplicate articles and those that did not meet the eligibility criteria as well as the inclusion of articles through a manual search, 13 articles were included in the review. Conducting a scoping review is the best way to map what is known about a subject. By focusing on the feedback perception more than the feedback itself, the results of this study will surely contribute to gaining a deeper understanding of how to proceed to enhance internal feedback and surgical residents' learning progress. Open Science Framework yexb; https://osf.io/yexkb. PRR1-10.2196/56727.

Trends and Predictors of Students’ Academic Performances by Levels of Study in University of Health and Allied Sciences in Ghana: A Longitudinal Surveillance Study Among a Cohort of 2020 Graduating Class of Undergraduates at the School of Allied Health Sciences in Ho North Campus

Background: Ascertainment of trends and predictors of academic performances among Allied Health students is critical to United Nation’s Sustainable Development Goals 3, 4 and 8. This article assesses the dynamics and academic risk factors associated with students’ performances at the University of Health and Allied Sciences (UHAS), Ghana. Method: This is a longitudinal cohort study, designed to evaluate retrospectively, the academic records of 180 students of the 2020 graduating class of undergraduates in the School of Allied Health Sciences. The published grades of each student from levels 100 to 400 were collated using a data extraction log that was quality controlled, double-entered and statistically analysed using Microsoft Visual Basic and STATA platforms respectively. The trends and predictors of academic performances by levels were determined using the Cochrane-Armitage test for trends and the Cochran-Martin-Haenszel test for association respectively. Results: The mean rates of scoring grades ‘A’, ‘B+’, ‘B’, ‘C’, ‘D’ and ‘E/F’ among this cohort were 22.2%, 21.2%, 23.3%, 25.8%, 7.9% and 0.5% respectively. The inter-variations in a proportion of grades within the levels were statistically significant (p<0.01). However, the intra-variations in proportional distribution of grades trended insignificantly across the levels (p>0.05). Although, the Spearman’s coefficient depicted a strong negative correlation between grade ‘A’ and levels (r= -0.9); the associations were only between grade ‘A’ and level 200s (OR=1.9; CI: 1.05-1. 33, p<0.001) and between grade ‘A’ and level 300 (OR=1.4;(CI: 1.25-1.59), p<0.001). Conclusion: The cumulative proportion of 43.4% students scoring grade ‘A’ and ‘B +’ reflected above average performances in SAHS. However the distribution of grades on Gaussian curve deviates the Central Limit Theorem. In addition, the observed negative correlation between trends of grade ‘A’ from lower (100) to high (400) levels of studies deviates Bloom’s and SOLO Taxonomies, and Trans-Theoretical Model (TTM) of assessing students’ learning progression. These phenomena have Quality Assurance, Academic Policy and Research implications. Therefore, the use of modify (hybrid) grading System is strongly recommended in SAHS. Also, we strongly recommend an extended study to determine factors that affect the poor performances of levels 100 and 400 students in SAHS.

Paper assessment or online assessment: exploring the impact of assessment modes on EFL students’ language learning outcomes and personal development

This research explores how paper and online assessments (OA) can affect EFL learners’ personal development variables such as self-esteem, mindfulness, demotivation, and language learning development. Sixty intermediate English as a foreign language (EFL) students participated in the current research and were randomly grouped into the experimental group (EG) and the control group (CG). Before the intervention, the data collection tools assessing language learners’ self-esteem, mindfulness, demotivation, and language learning development were administered as pre-tests to all the participants. Subsequently, the EG was taught all lessons of the coursebook, Top Notch 2, by utilizing OA while the CG was taught the identical lessons using a conventional (pen and paper) assessment. In the post-treatment phase, the two groups completed the post-tests on self-esteem mindfulness, demotivation, and language learning progress. The study demonstrated that the EG group outperformed the CG group on the four post-tests. The findings indicate that the intervention of OA significantly enhanced language learning outcomes and psychological well-being regarding the measured personal development variables. The enhanced efficacy in the EG group can be ascribed to the utilization of OA during the treatment phase, which has a beneficial effect on academic and personal development. This study also posits that integrating OA augments psychological engagement and language development among EFL learners. It highlights the implications of the results associating teaching EFL via OA with the observed personal development aspects and attributes them to effects of positive psychology.

The Effect of a Diagnostic System for Scientific Competency Combined with a Personalized Intelligent Tutoring Module through Machine Learning for Enhancing the Learning Progression of Seventh Grade Students

This study investigated the effects of a diagnostic system for scientific competency, combined with a personalized intelligent tutoring module that utilized machine learning, on the learning progression of seventh-grade students. The study employed a decision tree algorithm in Python to analyze secondary data from 847 students. The decision tree algorithm was used to create a predictive model that could diagnose individual scientific competency levels, which informed the development of individualized learning pathways. The experimental study involved 176 students, who were divided into two groups: an experimental group that received interactive feedback and a control group that did not receive any feedback. The study utilized a one-way MANOVA to evaluate three dimensions of scientific competency: Explanation of Scientific Phenomena, Evaluation and Design of Scientific Inquiry, and Interpretation of Data and Use of Evidence. The findings demonstrated significant improvements in the experimental group across all dimensions, highlighting the importance of immediate feedback in enhancing comprehension and motivation. The system was also highly rated for its quality and user satisfaction. This study emphasizes the potential of intelligent tutoring systems to improve scientific competency and suggests further educational applications.

Low-Stakes, Growth-Oriented Testing in Large-Enrollment General Chemistry 1: Formulation, Implementation, and Statistical Analysis.

We formulate an alternative to high-stakes examinations that is designed to help students grow, and we describe its implementation in a large-enrollment General Chemistry 1 class. In our alternative grading approach, students complete weekly assessments. Each assessment has four items that are aligned to explicit learning objectives and a level in Marzano's taxonomy, retrieval, comprehension, analysis, and knowledge utilization, which can be used by students and instructors to gauge the progression of student learning. Proficiency-based grading and multiple attempts reduce the stakes of the assessments. Unique assessments are generated through a computational infrastructure that draws question stems from an item bank and further randomizes quantities, elements, compounds, reactions, spectra, Lewis structures, orbitals, etc. in the questions. Nearly all assessment items require student-generated responses and cover a complete General Chemistry 1 curriculum. We interpret Marzano's taxonomy in the General Chemistry context and outline the structure of the learning objectives, cognitive levels, assessment schedule, and grading scheme. Item response theory (Rasch analysis) validates the theoretical framework and indicates that assessment items are high quality. Students demonstrate improvement through assessment retakes, and they report that the system motivates them to study and learn.

TransURL: Improving malicious URL detection with multi-layer Transformer encoding and multi-scale pyramid features

While machine learning progress is advancing the detection of malicious URLs, advanced Transformers applied to URLs face difficulties in extracting local information, character-level information, and structural relationships. To address these challenges, we propose a novel approach for malicious URL detection, named TransURL, that is implemented by co-training the character-aware Transformer with three feature modules—Multi-Layer Encoding, Multi-Scale Feature Learning, and Spatial Pyramid Attention. This special Transformer allows TransURL to extract embeddings that contain character-level information from URL token sequences, with three feature modules contributing to the fusion of multi-layer Transformer encodings and the capture of multi-scale local details and structural relationships. The proposed method is evaluated across several challenging scenarios, including class imbalance learning, multi-classification, cross-dataset testing, and adversarial sample attacks. The experimental results demonstrate a significant improvement compared to the best previous methods. For instance, it achieved a peak F1-score improvement of 40% in class-imbalanced scenarios, and exceeded the best baseline result by 14.13% in accuracy in adversarial attack scenarios. Additionally, we conduct a case study where our method accurately identifies all 30 active malicious web pages, whereas two pior SOTA methods miss 4 and 7 malicious web pages respectively. The codes and data are available at: https://github.com/Vul-det/TransURL/.

Empowering Higher Education Students: Enhancing Self-Regulated Learning through LMS Implementation

The purpose of this study is to prove and examine how to improve students' SRL skills through the implementation of LMS. The Learning Management System (LMS) is one of the most popular e-learning platforms. The effect of using LMS in education is to strengthen students' self-regulated learning (SRL). SRL is an individual's awareness to actively participate in organizing, controlling, and managing their own learning process. There are three aspects, might enhance students' SRL in terms of SRL aspects such as metacognition, motivation, and behavior. The research method used is descriptive-qualitative research on SPADA LMS. 80 students who utilized the LMS for learning were observed and interviewed by the researcher. The motivation aspect is proven by the grades; indirectly, this will motivate students to study well and get the appropriate grades. LMS also provides learning progress monitoring, allowing students to monitor their learning progress. The behavior aspect of SRL is implemented in the LMS with a deadline feature that forces students to work on assignments according to a predetermined time. This will force students to practice discipline and manage their time well. The metacognition aspect has been applied in LMS through assignment, discussion, and quiz features that force students to be responsible when performing activities given by lecturers. The results concluded that LMS has a positive contribution to enhancing students' SRL. By using LMS, students can manage their own study time and study strategy. This research indicates that if we want to achieve SRL, all aspects of SRL must be well fulfilled. This study has implications for all academics, learners, and institutions that have adopted LMS, particularly the institutions under study. The limitations of this study only focus on the role of LMS and factors that influence students’s SRL, especially at the higher education level in related universities.

Knowledge of English Affixes in Thai EFL Learners of Science and Language Programs

This study investigated the knowledge of affixes on vocabulary development among English as a Foreign Language (EFL) learners, focusing on the differentiation between students studying science and those studying the language. Affix knowledge, encompassing both prefixes and suffixes, is essential for expanding word family knowledge, serving as a cognitive bridge to proliferate word family members. To explore this, 111 secondary school students from a semi-urban Thai school, split into 53 science and 58 language program students, were assessed through receptive and productive affix knowledge tests. The findings reveal that science program students outperformed their language counterparts in receptive affix knowledge tests. Additionally, results indicated a learning continuum in affix acquisition, with students showing better average performance on receptive tests compared to more complex productive tasks. Notably, affix knowledge concerning prefixes was superior to that of suffixes across both test types. Moreover, the correlational analysis revealed a medium to strong relationship between English affix knowledge and vocabulary knowledge. The findings also suggest that suffix knowledge has a greater impact on productive vocabulary than on receptive vocabulary. Regression analysis supports the correlation results, highlighting that a deeper understanding of affixes is linked to stronger vocabulary knowledge in both comprehension and usage. These results suggest a structured progression in affix learning, from recognition to production, and underline the significance of affix knowledge in vocabulary expansion for EFL learners. The study emphasizes the need for further research into the mechanisms of affix acquisition and its role in language learning curricula.

Reflective Thinking on Enhancing Student Competencies in Learning Management through Outcome-Based Learning According to Constructive Alignment

This study investigates the implementation of learning outcomes-based education in mechanical engineering, focusing on students aspiring to pursue teaching careers. By using constructive alignment and reflective thinking methodologies, the study aims to enhance these students’ competencies in line with professional teaching standards. The study targets a group of 23 mechanical engineering students. The researcher’s established learning process forms the basis for the study methodology, incorporating learning objectives, active learning, and reflective thinking. The tools employed include a knowledge measurement test, activity evaluation through observation participation, follow-up on a permitted 5-level activity, reflection on learning experiences through writing, structuring learning activities in each subject, and assessment using qualitative content analysis. The results reveal significant variations in students’ academic achievements before and after the study, indicating moderate learning progress. The assessments of management learning performance are comprehensive, with learners engaging at the highest average level. This study underscores the significance of reflective learning approaches in developing competencies that align with professional teaching standards, enhancing students’ capacity to critically assess their thoughts, feelings, and attitudes, thereby influencing their actions and preparing them for real-world applications.

The English-language learning difficulties of older migrants in New Zealand: A life course perspective

ABSTRACT Older migrants are often later-life language learners by necessity. While the bulk of extant research on older language learners focuses on those who elect language learning as a constructive leisure pursuit, for older migrants with low proficiency in the dominant host country language, progress in language learning is a key factor that contributes to their independence, social inclusion, and well-being. Drawing on the life-course perspective, this article examines the language-learning difficulties (LLDs) of older Chinese migrants in New Zealand who have attended English courses, and who need basic communicative language ability in their daily lives. We identified three main life-course related factors that contribute to their language-learning difficulties and detract from their commitment to formal learning. These LLDs result from perceived shortcomings in individuals’ prior educational background, factors related to individuals’ current circumstances that impact their sense of agency, and unsatisfactory current formal learning experiences. We offer recommendations aimed at improving formal learning opportunities for this learner group.

CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation Using Graph Neural Networks

Electronic Health Records (EHRs) play a crucial role in shaping predictive are models, yet they encounter challenges such as significant data gaps and class imbalances. Traditional Graph Neural Network (GNN) approaches have limitations in fully leveraging neighbourhood data or demanding intensive computational requirements for regularisation. To address this challenge, we introduce CliqueFluxNet, a novel framework that innovatively constructs a patient similarity graph to maximise cliques, thereby highlighting strong inter-patient connections. At the heart of CliqueFluxNet lies its stochastic edge fluxing strategy — a dynamic process involving random edge addition and removal during training. This strategy aims to enhance the model’s generalisability and mitigate overfitting. Our empirical analysis, conducted on MIMIC-III and eICU datasets, focuses on the tasks of mortality and readmission prediction. It demonstrates significant progress in representation learning, particularly in scenarios with limited data availability. Qualitative assessments further underscore CliqueFluxNet’s effectiveness in extracting meaningful EHR representations, solidifying its potential for advancing GNN applications in healthcare analytics.

The Power of Wondering What Could Be School

Early in the 2023–2024 school year, Mr. Medeiros and a handful of his Kaua’i High School Future Teachers of Hawai’i club members took a two-day huaka’i (trip) to Oahu. This service-learning trip was part of the learning progression for the club, which is focused on student advocacy and social justice. With the help of Josh Reppun, retired Hawai’i State Supreme Court Justice Mike Wilson, the people at What School Could Be, and other amazing educators, the students began their huaka’i by gathering together to think and talk about the purpose of education/school. Ostensibly, they were traveling to Oahu to learn from others, but, really, the adults were there to learn how to keep pushing the boundaries of the idea of “school.”

Enhancing students' learning outcomes in self‐regulated virtual reality learning environment with learning aid mechanisms

AbstractVirtual Reality (VR) has demonstrated significant advantages in various educational fields as it allows learners to actively learn in high interactivity and realism under their learning pace. However, for low‐achieving students, VR learning environments may introduce some challenges, which are problems encountered during their learning process, which lead to difficulties in self‐regulating their learning progress and becoming disoriented in the lack of guidance. This study aims to explore the improvement of self‐regulated VR learning environment based on the Cognitive Theory of Multimedia Learning and enhance the performance of low‐achieving students. We designed a VR course on electronic circuit hardware and programming, providing participants with a series of learning aids in the VR learning environment, including feedback, hints and guidance. Feedback aids provide correct or incorrect feedback after each task or test, guidance aids display progress following each task or test and hint aids offer guidance when inactivity is detected. A quasi‐experiment was conducted by using questionnaires and practical tasks to assess the participants' cognitive levels, practical hands‐on skills, self‐regulated learning abilities and learning engagement after VR learning. The results demonstrated significant improvements in all learning indicators for low‐achieving students. Practitioner notesWhat is already known about this topic Virtual Reality (VR) presents itself as a promising tool for self‐regulated learning (SRL). SRL abilities are of paramount importance within the learning environment of VR. In the context of the self‐regulated Virtual Reality (SRVR) learning environment, low‐achieving students have received little attention. What this paper adds It examines the effectiveness of learning aid mechanisms within the SRVR learning environment. These mechanisms support cognitive levels and increase engagement in SRVR learning. Low‐achieving learners benefit from the presence of learning aid mechanisms in the SRVR learning environment. Implications for practice and/or policy Providing learning aid mechanisms within the SRVR learning environment can potentially augment learning. Learning aids mechanism have the potential to provide comprehensive support within the SRVR setting.

Advanced computer architecture optimization for machine learning/deep learning

Abstract The recent progress in Machine Learning (Géron, 2022) and particularly Deep Learning (Goodfellow, 2016) models exposed the limitations of traditional computer architectures. Modern algorithms demonstrate highly increased computational demands and data requirements that most existing architectures cannot handle efficiently. These demands result in training speed, inference latency, and power consumption bottlenecks, which is why advanced methods of computer architecture optimization are required to enable the development of ML/DL-dedicated efficient hardware platforms (Engineers, 2019). The optimization of computer architecture for applications of ML/DL becomes critical, due to the tremendous demand for efficient execution of complex computations by Neural Networks (Goodfellow, 2016). This paper reviewed the numerous approaches and methods utilized to optimize computer architecture for ML/DL workloads. The following sections contain substantial discussion concerning the hardware-level optimizations, enhancements of traditional software frameworks and their unique versions, and innovative explorations of architectures. In particular, we discussed hardware including specialized accelerators, which can improve the performance and efficiency of a computation system using various techniques, specifically describing accelerators like CPUs (multicore) (Hennessy, 2017), GPUs (Hwu, 2015) and TPUs (Contributors, 2017), parallelism in multicore architectures, data movement in hardware systems, especially techniques such as caching and sparsity, compression, and quantization, other special techniques and configurations, such as using specialized data formats, and measurement sparsity. Moreover, this paper provided a comprehensive analysis of current trends in software frameworks, Data Movement optimization strategies (A.Bienz, 2021), sparsity, quantization and compression methods, using ML for architecture exploration, and, DVFS (Hennessy, 2017),, which provides strategies for maximizing hardware utilization and power consumption during training, machine learning, dynamic voltage, and frequency scaling, runtime systems. Finally, the paper discussed research opportunity directions and the possibilities of computer architecture optimization influence in various industrial and academic areas of ML/DL technologies. The objective of implementing these optimization techniques is to largely minimize the current gap between the computational needs of ML/DL algorithms and the current hardware’s capability. This will lead to significant improvements in training times, enable real-time inference for various applications, and ultimately unlock the full potential of cutting-edge machine learning algorithms.

Renewable Energy Prediction Using Different ML Algorithms

Abstract: The global community is grappling with numerous obstacles in fulfilling its energy requirements, stemming from the limited availability of fossil fuel reserves. It is, at present, rapidly shifting its focus towards Renewable Energy Sources to overcome the limitations of fossil fuels and generate environmentally friendly energy. Earlier, their unpredictable nature made them less appealing, but recent progress in Artificial Intelligence (AI) and Machine Learning (ML) has allowed for accurate forecasts of their energy production. With a well-planned approach to balancing energy loads, renewable energy sources can satisfy energy needs without the necessity for extra storage. In this piece, we conducted a machine learning analysis on a dataset obtained from the Kaggle Platform by utilizing various algorithms, such as Random Forest (RF), Linear Support Vector Regression (SVM), Decision Tree (DT), and Linear Regression (LR).

The New Mixed Teaching Exploration of Low-Voltage Electrician Training Course under the Background of New Engineering

In the context of New Engineering, the exploration of a novel blended teaching approach for low-voltage electrician training courses aims to address the challenges of modern engineering education by integrating New Engineering concepts and modern teaching technologies to achieve comprehensive innovation in course content and teaching methods. This strategy leverages the advantages of combining online and offline learning to provide a diverse and personalized learning experience, thereby enhancing students' knowledge and practical skills. Online course resources, including interactive courseware, virtual experiments, and simulations, enrich the learning content and increase students' interest and engagement. Offline classes focus on interactivity and practical application through discussions, case studies, and project collaborations, helping students deepen their understanding and apply their knowledge. Flexible scheduling allows students to learn at their own pace, improving learning efficiency and effectiveness. Personalized learning path design, through preliminary surveys and assessments, helps students set individualized learning goals. Utilizing big data analysis and intelligent recommendation algorithms, the system provides the most suitable learning resources. Students can autonomously adjust their learning paths, fostering self-directed learning, while teachers offer targeted guidance and support to ensure the successful completion of learning tasks. Interactive and collaborative learning environments are strengthened through online and offline interaction mechanisms, group collaborative learning, and the use of various interactive tools, enhancing classroom interaction and participation. The establishment of online learning communities promotes resource sharing and experience exchange, creating a positive learning atmosphere. Assessment and feedback employ formative and practical assessments through online tests, assignments, and project work, comprehensively reflecting students' learning progress and outcomes. Multi-dimensional feedback mechanisms and data-driven evaluation provide detailed learning analysis and improvement suggestions for both teachers and students, enhancing teaching effectiveness.

Testing the Construct Validity and Reliability of the Student Learning Motivation Scale Using Confirmatory Factor Analysis (CFA)

Constructivist learning environments are created by utilizing constructivist theory to raise teaching standards and motivate students to learn more. The low level of student motivation in the learning process is caused by several problems that occur. Thus, learning motivation is very important in the progress of learning, namely to encourage enthusiasm during the learning process. This research aims to measure students' learning motivation. The sample for this research was fourth grade elementary school students. This research uses a quantitative approach designed using measuring instruments in the form of questionnaires. The results of the research show that these four aspects are: the desire and desire to succeed, the appreciation for learning, the desire to be interested in learning, and optimism. There is one indicator that does not meet the criteria, namely the reward aspect of learning. Therefore, the three aspects that have been described are proven to meet the criteria of goodness of fit, validity and construct reliability. Therefore, the scale developed is suitable for use in collecting data to measure student learning motivation.

Integrating constructivist principles in an adaptive hybrid learning system for developing social entrepreneurship education among college students

Education in social entrepreneurship for college students is a gradual and evolving process that shapes their career outlook and skill development. This journey involves employing various strategies to engage with and analyze innovative concepts. Adaptive hybrid learning systems adjust course materials dynamically based on individual students' skills and learning progress, drawing from diverse learning theories and instructional interventions. Empowering students to drive positive social change is a central focus, with social entrepreneurs tackling global challenges such as poverty, unemployment, gender inequality, inadequate education, healthcare, and governance. While some students may find traditional classroom settings inconvenient and prefer the flexibility of hybrid learning, others weigh the risks and benefits of classroom instruction differently. Data Envelopment Analysis (DEA) is utilized for empirical analysis, utilizing early-stage social entrepreneurship education as a performance measure and cultural probabilities as variables. The significance of college students' contributions through social enterprises is increasingly recognized, prompting the development of Convolutional Neural Network (CNN) models to forecast and assess career growth trends. The DEA-CNN framework aims to enhance adaptive hybrid learning systems by evaluating social entrepreneurs' knowledge, skills, and competencies. Social entrepreneurship not only addresses environmental concerns but also enriches cultural diversity. Many college students prioritize practical action over mere ideation, particularly in entrepreneurship.

Navigating egalitarian culture and accountability pressures: shared instructional leadership practices of Danish school leaders

PurposeOur study presents insights from an exploratory qualitative case study conducted in three primary and lower secondary schools in Denmark, a country renowned for its collaborative and egalitarian culture, to unravel the complexities of shared instructional leadership.Design/methodology/approachInterviews with principals, middle leaders, and teachers and document analysis were used to identify themes according to shared and instructional leadership perspectives.FindingsThe study yielded three major findings. First, Danish principals structure schools to prioritize student learning outcomes and distribute responsibility to middle leaders and teachers. Second, reflection among teachers and leaders better prepares them for future demands and obligations. Third, collaboration underpins principals’ vision of reflection and professional development.Practical implicationsThe research team’s reflection on the data collected can be used to build future strategies to address unpredictable student learning progression and poor-performing teachers.Originality/valueTogether, these findings contribute to the broader understanding of shared instructional leadership and demonstrate how principals face external pressure for accountability and how egalitarian culture influences principals’ practices.

Entertainment social media based on deep learning and interactive experience application in English e-learning teaching system

With the rapid development of the Internet, e-learning is becoming more and more popular as a convenient and flexible way of learning. This study aims to explore the application of entertainment social media based on deep learning and interactive experience in English e-learning teaching system to improve students’ learning effect and motivation. The interface design of e-learning system incorporates gamification elements, and deep learning algorithms are used to analyze students’ learning habits and progress. The system can provide personalized learning paths and recommended content for each student to ensure that learning resources match students’ needs and abilities. A variety of interactive learning activities are built into the system to encourage students to actively participate in and practice their English speaking and listening skills. With integrated social media capabilities, students can create profiles on the learning platform, join interest groups, communicate with peers and study collaboratively. Through the data analysis of experiments and questionnaires, we found that entertainment social media based on deep learning and interactive experience can effectively improve students’ learning interest and motivation in the English e-learning teaching system. Students showed higher engagement and motivation when using the system and achieved better learning outcomes.