Assisted Learning Research Articles

Reading of ChatGPT Features Description Alters Expectations and Learning Performance of Dental Students

ObjectivesThe number of approvals for AI-based systems is increasing rapidly, although AI clinical trial designs lack consideration of the impact of human–AI interaction. Aims of this work were to investigate how reading of an AI system (ChatGPT) features description could influence willingness and expectations for use of this technology as well as dental students’ learning performance. MethodsDental students (N = 104) were asked to learn about side effects of drugs used in dental practice via reading recommended literature or ChatGPT. Expectations towards ChatGPT were measured by survey, before and after reading of a system features description, whilst learning outcomes were evaluated via pharmacology quiz. ResultsStudents who used ChatGPT (YG condition) showed better results on the pharmacology quiz than students who neither read the description nor employed ChatGPT for learning (NN condition). Moreover, students who read the description of ChatGPT features yet did not use it (NG) showed better results on the pharmacology quiz compared with the NN condition, although none of them employed ChatGPT for learning. The NG students compared to the YG students had less trust in AI system assistance in learning, and after the AI system description reading, their expectations changed significantly, showing an association with quiz scores. ConclusionsStudents were reluctant to use ChatGPT, but ChatGPT features description reading could alter expectations and enhance learning performance of students; this suggests AI description–related cognitive bias. The description content should be reviewed and verified prior to AI system use, whilst AI system features description introduction should be considered for all participants in the clinical trial.

Learning media projects with YouTube videos: a dynamic tool for improving mathematics achievement

<span lang="EN-US">This research was designed with a descriptive research approach. The research objectives of the study were to: i) investigate the impact of the project task of learning video media on prospective teachers and students; ii) analyze the important role of instructional video media as a learning assistant for students; and iii) describe the function of instructional video media in increasing students’ interest and achievement in learning. This research involved 100 participants consisting of 10 students of a teacher education study program (prospective teachers), and 90 junior high school students. The researchers collected data through tests; questionnaires; and interviews. Researchers analyzed the data through descriptive statistical analysis. The results of the study indicated that the project assignment of making videos for learning (on a YouTube channel) provided a positive impact on learning experiences for teachers (prospective teachers) and students. Mathematics learning video media has a role as a digital native that may help overcome the limitations of students’ knowledge and understanding. Mathematics learning videos on the YouTube channel can be a dynamic tool to increase students’ interest (as an attention, and affective function) and to improve students’ mathematics learning achievement (as a cognitive function).</span>

인공지능 기반 맞춤형 학습이 농촌지역 학생들의 수학학습에 미치는 효과

This study was conducted to propose customized learning methods based on artificial intelligence as an option to bridge the learning gap caused by COVID-19 and strengthen the learners' self-learning methods, as well as verify their effectiveness. In particular, the study was conducted with the aim of ensuring high-quality public education with AI technology in rural areas, where educational infrastructure is insufficient. The AI-based customized learning assistance consisted of an AI diagnostic assessment to examine the level of understanding of the prerequisite concepts before starting a study unit, AI-based problem recommendations on learning by the level, and AI assessment and supplementary learning aid at the end of the unit. Before and after conducting of the study, the math academic achievement, math learning attitude, and math self-directed learning ability were tested to examine the effectiveness of the program.The results of the data analysis showed that AI-customized math learning had a positive effect on improving the math academic achievement, learning attitude, and self-directed learning ability. However, it is still limited as to fragmented learning based on problem solving, and it is necessary to explore the AI education practices which would support continuous and integrated growth of math learners.

The Effect of CTL Assisted e-LKPD on Increasing Students' Motivation and Learning Outcomes

The purpose of this study was to determine the effect of e-LKPD assisted CTL learning on student motivation and learning outcomes. Samples were taken by purposive sampling as many as 2 classes, namely experimental classes and control classes. The instruments used were multiple choice tests and motivation questionnaires. Data on student learning outcomes and motivation were analyzed by right-side t-test and correlation test. Correlation test to see the relationship between learning motivation and learning outcomes. The results of the cognitive t-test obtained tcount > ttable (3.401> 1.668) which shows Ho is rejected and Ha is accepted, meaning that there is an effect of the CTL learning model assisted by e-LKPD on the learning outcomes of students on acid-base material. The t-test results of learning motivation obtained tcount> ttable (2.191> 1.668) which shows Ho is rejected and Ha is accepted, meaning that there is an effect of CTL learning assisted by e-LKPD on student learning motivation. The correlation results obtained tcount> ttable (0.495> 0.339) indicate that student learning motivation is positively and significantly correlated with student learning outcomes using CTL learning assisted by e-LKPD.

느린 학습자의 문화예술 체험 활동을 통한 문화예술교육 지원 방안 연구

The purpose of this study is to identify the characteristics of slow learners who are overlooked by institutional frameworks and to examine how cultural and arts experiences can facilitate their cognitive, affective and physical development. This research aims to propose strategies to support cultural and arts education tailored for slow learners. To achieve this, interviews were conducted with parents, primary school and middle school teachers, counselors, cultural and arts center and educational institutions managers and art instructors who have provided cultural and arts experience education to slow learners. The analysis results are organized by separating them into individual and institutional dimensions. At the individual level, the findings indicate the need for cultural and arts activities that enable slow learners to experience a sense of achievement. Since slow learners are educated alongside regular students, they often feel a relatively lower sense of achievement across cognitive, affective, and physical domains, leading to a loss of motivation to learn. Additionally, the study suggests expanding opportunities for communication among students, and between teachers and students, to make them more comfortable with expressing themselves internally. This involves providing numerous opportunities for self-expression by leveraging the variety and enjoyment of communication activities. Furthermore, the active use of play-based learning is advocated. Engaging in play involves all domains—cognitive, affective, and physical—and increases the chance for achievement due to the diversity of levels. At the institutional level, the findings can be summarized as follows. First, there is a need for policy support for cultural and arts experience education for slow learners, a point that has been highlighted in various studies. Without this support, the social costs could become substantially high. Secondly, there is a call for support within the framework of lifelong education. The principle of lifelong education is based on self-directed learning, where individuals select the content and level of their learning. However, self-directed learning poses challenges for slow learners, necessitating the need for learning assistants. This area requires institutional support via policy backing.

Leveraging undergraduate learning assistants when implementing new laboratory curricula

At University of California, Irvine, a large-enrollment research university, undergraduate chemistry courses for non-chemistry majors were delivered remotely during the 2020–2021 academic year, with a return to in-person instruction planned for January 2022. Because this return to in-person instruction coincided with the transition of second-year students from general chemistry to organic chemistry laboratory courses, the instructional staff recognized a need for remedial laboratory curricula for students with no prior in-person laboratory experience. Simultaneously, we desired to implement undergraduate Learning Assistants (LAs) in non-chemistry major organic chemistry laboratories for the first time at our university. In this paper, we describe our approach for leveraging undergraduate LAs to (1) test new laboratory curricula and (2) address feelings of comfort and safety for students with no prior in-person laboratory experience. Benefits of our LA program perceived by students include increased laboratory efficiency and improved student learning from near-peer instructors; benefits perceived by LAs include the development of professional skills and teamwork with graduate student teaching assistants. We provide an outline of resources and strategies to enable instructors to simultaneously implement undergraduate LAs and new laboratory curricula.

Image rectangling network based on reparameterized transformer and assisted learning

Stitched images can offer a broader field of view, but their boundaries can be irregular and unpleasant. To address this issue, current methods for rectangling images start by distorting local grids multiple times to obtain rectangular images with regular boundaries. However, these methods can result in content distortion and missing boundary information. We have developed an image rectangling solution using the reparameterized transformer structure, focusing on single distortion. Additionally, we have designed an assisted learning network to aid in the process of the image rectangling network. To improve the network’s parallel efficiency, we have introduced a local thin-plate spline Transform strategy to achieve efficient local deformation. Ultimately, the proposed method achieves state-of-the-art performance in stitched image rectangling with a low number of parameters while maintaining high content fidelity. The code is available at https://github.com/MelodYanglc/TransRectangling.

Enhanced Educational Experiences through Personalized and AI-based Learning

Academic motivation is a pivotal factor in shaping students' educational trajectories. Mathematics education holds a unique position as a school subject, due to its influence on students' grade transitions and cognitive development at both basic and advanced levels. From March to May 2023, the AI Education (AIEDN) research project investigated how an AI-based learning assistant can enhance students’ understanding of the subject matter through video-based learning. For this study, 275 students were selected in the age range of 14-20 from two secondary (N=137) and two grammar schools (N=138) in Baden-Württemberg, Germany. The quantitative experiment tested the extent to which learners solve more tasks, build broader (transfer) knowledge, and retain it. Students were given a set of mathematical problems on an unknown topic to solve in 90 minutes. The test group used the AI assistant to ask questions, while the control group used only keyword searches. The results of the t-test indicated that the test group who used the AI Learning assistant in the advanced course achieved a significantly strong (Cohen’s d=.63) increase in performance results (T(19)=-2.82; p.01). Conversely, the advanced course control group as well as both the test and control groups of the basic course(s) showed no significant improvements.

Sosialisasi Peraturan Daerah Provinsi DKI Jakarta Tentang Kependidikan

The aim of community service activities is to help the community facilitate the provision of information related to education in the DKI Jakarta provincial government. The methods used in this activity are: a. Direct observation means that the service member comes directly to the service location to collect data. b. Carrying out community service activities by conducting outreach and training to community service partners. c. Carrying out evaluations and sharing together to produce final results of activities. The results of the community service activities that have been carried out are that the community from the community service partners provide feedback to us, the service team, namely input that supports the continuation of community service activities. The conclusions from the community service activities as follows: educational outreach activities in the DKI Jakarta provincial government were very well received by the participants with many giving suggestions for additional scholarships for outstanding students and other learning assistance

Reinforcement learning in autonomous driving

Automatic driving technology has become a highly researched field in recent years, aiming to achieve vehicle driving without human intervention. In this regard, reinforcement learning techniques have played a crucial role. This study discusses and analyses the use of reinforcement learning in automatic driving methods. The research begins with the process of reinforcement learning. In the architectural framework, there is a special emphasis on designing innovative reward functions to encourage safe and socially acceptable driving behaviour, while considering uncertainty factors through advanced Bayesian neural networks. This paper primarily focuses on aspects such as scene understanding, localization and mapping, planning and driving strategies, and control. Furthermore, the paper analyses the key elements of automatic driving and delves into the specific complexities associated with each element. It highlights the utilization of reinforcement learning within the realm of autonomous driving. Reinforcement learning assists autonomous vehicles in understanding the surrounding environment, accurately identifying paths, making intelligent driving decisions, and safely controlling the vehicle. Reinforcement learning especially working with deep learning plays a crucial role in realizing and continuously improving automatic driving. Finally, the paper concludes with a summary and outlook on the entire study.

Characteristics of spatial and temporal variability in the distribution of biological soil crusts on the Loess Plateau, China.

Biological soil crust (biocrust) is widely distributed on the Loess Plateau and plays multiple roles in regulating ecosystem stability and multifunctionality. Few reports are available on the distribution characteristics of biocrust in this region, which limits the assessment of its ecological functions. Based on 388 sampling points in different precipitation zones on the Loess Plateau from 2009 to 2020, we analyzed the coverage, composition, and influencing factors of biocrust across different durations since land abandonment, precipitation levels, topography (slope aspect and position), and utilization of abandoned slopelands (shrubland, forest, and grassland). On this base, with the assistance of machine learning and spatial modeling methods, we generated a distribution map of biocrust and its composition at a resolution of 250 m × 250 m, and analyzed the spatial distribution of biocrust on the Loess Plateau. The results showed that the average biocrust coverage in the woodlands and grasslands was 47.3%, of which cyanobacterial crust accounted for 25.5%, moss crust 19.7%, and lichen crust 2.1%. There were significant temporal and spatial variations. Temporally, the coverage of biocrust in specific regions fluctuated with the extension of the abandoned durations and coverage of cyanobacterial crust, while moss crust showed a reverse pattern. In addition, the coverage of biocrust in the wet season was slightly higher than that in the dry season within a year. Spatially, the coverage of biocrusts on the sandy lands area on the Loess Plateau was higher and dominated by cyanobacterial crusts, while the coverage was lower in the hilly and gully area. Precipitation and utilization of abandoned land were the major factors driving biocrust coverage and composition, while slope direction and position did not show obvious effect. In addition, soil organic carbon content, pH, and texture were related to the distribution of biocrust. This study uncovered the spatial and temporal variability of biocrust distribution, which might provide important data support for the research and management of biocrust in the Loess Plateau region.

Circular Dichroism Sensing: Strategies and Applications.

The analysis of the absolute configuration, enantiomeric composition, and concentration of chiral compounds are frequently encountered tasks across the chemical and health sciences. Chiroptical sensing methods can streamline this work and allow high-throughput screening with remarkable reduction of operational time and cost. During the last few years, significant methodological advances with innovative chirality sensing systems, the use of computer-generated calibration curves, machine learning assistance, and chemometric data processing, to name a few, have emerged and are now matched with commercially available multi-well plate CD readers. These developments have reframed the chirality sensing space and provide new opportunities that are of interest to a large group of chemists. This review will discuss chirality sensing strategies and applications with representative small-molecule CD sensors. Emphasis will be given to important milestones and recent advances that accelerate chiral compound analysis by outperforming traditional methods, conquer new directions, and pioneering efforts that lie at the forefront of chiroptical high-throughput screening developments. The goal is to provide the reader with a thorough understanding of the current state and a perspective of future directions of this rapidly emerging field.

Circular Dichroism Sensing: Strategies and Applications

AbstractThe analysis of the absolute configuration, enantiomeric composition, and concentration of chiral compounds are frequently encountered tasks across the chemical and health sciences. Chiroptical sensing methods can streamline this work and allow high‐throughput screening with remarkable reduction of operational time and cost. During the last few years, significant methodological advances with innovative chirality sensing systems, the use of computer‐generated calibration curves, machine learning assistance, and chemometric data processing, to name a few, have emerged and are now matched with commercially available multi‐well plate CD readers. These developments have reframed the chirality sensing space and provide new opportunities that are of interest to a large group of chemists. This review will discuss chirality sensing strategies and applications with representative small‐molecule CD sensors. Emphasis will be given to important milestones and recent advances that accelerate chiral compound analysis by outperforming traditional methods, conquer new directions, and pioneering efforts that lie at the forefront of chiroptical high‐throughput screening developments. The goal is to provide the reader with a thorough understanding of the current state and a perspective of future directions of this rapidly emerging field.

Improved DRL-based energy-efficient UAV control for maximum lifecycle

Unmanned aerial vehicles (UAVs) operating as airborne base stations (UAV-BSs) provide efficient on-demand services to ground users. UAV-BSs are inherently flexible and mobile, allowing them to be strategically deployed based on ground user distribution and quality of service requirements, including coverage rate, system lifecycle, and user fairness. Owing to the limited battery capacity and coverage range of the UAVs, managing them to extend their operational lifecycle, ensure service fairness, and maintain a specific real-time coverage rate is challenging. Therefore, a multi-objective optimization problem with constrained Pareto dominance is formulated. Subsequently, a novel assisted deep reinforcement learning model is developed to maximize the minimum remaining energy while simultaneously considering user fairness and coverage-rate requirements. The particle swarm optimization algorithm is adopted to assist multi-agent cooperative deep reinforcement learning. Finally, the simulation results show that the proposed model outperforms the other popular methods in terms of user fairness, system lifecycle, coverage rate, and energy efficiency in the context of multi-objective, multi-agent cooperative coverage control deployment.

Speech recognition based on mobile sensor networks application in English education intelligent assisted learning system

With the popularity of English learning and the development of mobile technology, the development of intelligent assisted learning system has become an effective learning method. The purpose of this study is to improve the speech recognition performance of English education intelligent learning assistance system by using mobile sensor networks, and improve the learning effect of learners. This paper uses a speech recognition algorithm based on mobile sensor network to acquire learners' speech signals with multiple sensor nodes. By preprocessing and feature extraction of acquired signals, machine learning algorithm is applied to recognize them, thus improving the recognition accuracy. The experimental results show that compared with traditional speech recognition methods, the speech recognition system based on mobile sensor network has better performance in the case of background noise and speaker change. When learners use the system to learn English, they can get more accurate and reliable speech recognition results, improve the learning effect and interactive experience, and provide a more effective learning tool for English learners.

Layers of Student Understanding Based on Pirie Kieren's theory in Solving Story Problems in Terms of Cognitive Style

Objective: Understanding lines and angles serves as the foundation for further mathematical topics such as trigonometry, geometry, calculus, etc. Students may struggle with advanced mathematical reasoning and problem-solving without a firm grasp of these basic concepts. This study aims to investigate how students' mathematical understanding processes are based on the folding back theory. Method: The research utilizes a qualitative approach with descriptive eksploratif design. Two subjects were selected from 28 seventh-grade students, each representing the field-dependent and field-independent cognitive style. Data was collected through mathematical comprehension tests, GEFT tests, and interviews. Data were analyzed through data reduction, data presentation, and verification stages, with each subject being interviewed to verify the processes. Results: Based on the research results, students in the field-independent cognitive style were much more active and better understood the problem-solving process than those in the field-dependent. However, both subjects still required learning assistance. Novelty: This research explores the folding back theory in the mathematical understanding process based on cognitive styles, whereas previous studies have mainly focused on mathematical comprehension abilities. Therefore, further research would benefit from using instructional media to better engage students in understanding the material.

The Influence of Learning Assistants on Faculty Use of Student-Centered Instruction

The Influence of Learning Assistants on Faculty Use of Student-Centered Instruction

Beamforming Optimization with the Assistance of Deep Learning in a Rate-Splitting Multiple-Access Simultaneous Wireless Information and Power Transfer System with a Power Beacon

This study examined the implementation of rate-splitting multiple access (RSMA) in a multiple-input single-output system using simultaneous wireless information and power transfer (SWIPT) technology. The coexistence of a base station and a power beacon was considered, aiming to transmit information and energy to two sets of users. One set comprises users who solely harvest energy, whereas the other can decode information and energy using a power-splitting (PS) structure. The main objective of this optimization was to minimize the total transmit power of the system while satisfying the rate requirements for PS users and ensuring minimum energy harvesting (EH) for both PS and EH users. The non-convex problem was addressed by dividing it into two subproblems. The first subproblem was solved using a deep learning-based scheme, combining principal component analysis and a deep neural network. The semidefinite relaxation method was used to solve the second subproblem. The proposed method offers lower computational complexity compared to traditional iterative-based approaches. The simulation results demonstrate the superior performance of the proposed scheme compared to traditional methods such as non-orthogonal multiple access and space-division multiple access. Furthermore, the ability of the proposed method to generalize was validated by assessing its effectiveness across several challenging scenarios.

Organohydrogel‐Based Soft SEMG Electrodes for Algorithm‐Assisted Gesture Recognition

AbstractEpidermal electronics that can monitor physiological signals such as surface electromyogram (sEMG) signals attract widespread attentions in personalized healthcare, human–machine interfaces (HMI) and virtual/augmented reality (AR/VR). However, conventional electromyographic electrodes suffer from skin discomfort, susceptibility to motion artifact interference, and short service lifetime. Here, an organohydrogel‐based sEMG electrode endows with high conductivity, low modulus and long‐term stability is developed by doping partially reduced graphene oxide (pRGO) into highly cross‐linked organohydrogel network. The as‐fabricated polyacrylamide/sodium alginate/tannic acid/partially reduced graphene oxide (PAM/SA/TA/pRGO) organohydrogel possesses farewell conductivity (4.22 S m−1) while preserving tissue‐like compliance (Young's modulus ≈32 KPa), excellent stretchability (≈600%), high adhesion as well as superior anti‐drying properties. In addition, a stretchable sEMG electrode for long‐term reliable service is fabricated via immobilizing the organohydrogel electrodes onto a flexible very high bond (VHB) substrate. As a result, the integrated electrodes show high signal‐to‐noise ratio (SNR) (35.15 db) comparable to that of the commercial electrodes. Furthermore, with assistance of deep learning, the proposed sEMG electrodes obtain high identification accuracy of 97.11% in distinguishing sophisticated gestures. This system can be further exploited for real‐time tele‐operations and offers broad prospects in human–machine immersive interactive application.

Pengembangan media pembelajaran Articulate Storyline untuk pemahaman konsep materi turunan

Kemampuan pemahaman konsep matematis siswa yang masih tergolong rendah memerlukan suatu penanganan berupa pembelajaran yang sesuai dengan karakteristik siswa dan materi pembelajaran. Hal itu dapat dilakukan dengan menghadirkan media pembelajaran yang difasilitasi oleh guru sebagai produk saintifik hasil desain dan pengembangan program multimedia interaksi Articulate Storyline. Penelitian ini bertujuan : (1) mendeskripsikan proses dan menghasilkan produk media pembelajaran Articulate Storyline yang valid dan praktis; (2) mengkaji kepraktisan implementasi pembelajaran menggunakan produk media pembelajaran Articulate Storyline (3) mengkaji peningkatan kemampuan pemahaman konsep matematis siswa pada materi Turunan yang mengikuti pembelajaran berbantuan media pembelajaran Articulate Storyline. Penelitian ini menggunakan metode pengembangan dengan model prosedural ADDIE: Analysis, Design, Development, Implementation, dan Evaluation. Penelitian melibatkan 32 siswa kelas XI di Konawe Selatan menggunakan Instrumen lembar tes, angket, dan validasi. Data penelitian dianalisis secara deskriptif dan inferensial. Hasil penelitian pengembangan media pembelajaran berbasis Articulate Storyline didapatkan valid dan sangat layak berdasarkan penilaian dari ahli materi dengan skor rata-rata keseluruhan 92% dan ahli media diperoleh skor rata-rata keseluruhan 89%. Penilaian kepraktisan media pembelajaran diperoleh skor rata-rata 82% yang termasuk kategori sangat praktis. Hasil analisis kepraktisan diperoleh kategori praktis berdasarkan respon siswa dan guru melalui implementasi pembelajaran. Hasil analisis peningkatan kemampuan pemahaman konsep turunan didapatkan rerata N-gain sebesar 0,548 yang dapat dimaknai bahwa kemampuan pemahaman konsep pada pembelajaran dengan media ini mengalami peningkatan dengan kategori sedang rata-rata hasil test kemampuan pemahaman konsep siswa adalah 66,4 hasil pretest dengan nilai minimun 62,2; maximum 75,6 yang berada pada kategori kemampuan pemahaman konsep siswa yang cukup. Rata-rata postest kemampuan pemahaman konsep siswa adalah 84,9 dengan nilai minimun 73,3; maximum 93,3 yang berada pada kategori kemampuan pemahaman konsep yang tinggi. Produk media pembelajaran Articulate Storyline pada materi Turunan diharapkan dapat menjadi alternatif media pembelajaran pada siswa kelas XI SMA. The ability to understand students' mathematical concepts that are still relatively low requires a handling in the form of learning in accordance with student characteristics and learning material. This can be done by presenting learning media facilitated by teachers as scientific products resulting from the design and development of multimedia programs for Articulate Storyline interaction. This study aims to: (1) describe the process and produce valid and practical Articulate Storyline learning media products; (2) examine the practicality of learning implementation using Articulate Storyline learning media products (3) examine the improvement of students' ability to understand mathematical concepts in derivative material that follows learning assisted by Articulate Storyline learning media. The research data were analyzed descriptively and inferentially. This research uses development methods with ADDIE procedural models: Analysis, Design, Development, Implementation, and Evaluation. The study involved 32 students of grade XI Science using test sheet instruments, questionnaires, and validation. The results of research on the development of learning media based on Articulate Storyline were obtained valid and very feasible based on the assessment of material experts with an overall average score of 92% and media experts obtained an overall average score of 89%. Assessment of the practicality of learning media obtained an average score of 82% which is included in the very practical category. The results of the practicality analysis obtained practical categories based on student and teacher responses through the implementation of learning. The results of the analysis of increasing the ability to understand derivative concepts obtained an average N-gain of 0.548 which can be interpreted that the ability to understand concepts in learning with this media has increased with the medium category, the average test result of the student's concept understanding ability is 66.4 pretest results with a minimum value of 62.2; Maximum 75.6 which is in the category of sufficient student concept understanding ability. The average postest of students' concept comprehension ability is 84.9 with a minimum score of 73.3; Maximum 93.3 which is in the category of high concept understanding ability. Articulate Storyline learning media products on Derivative material are expected to be an alternative learning media for grade XI high school students