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Journal articles on the topic 'Learning assistance'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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1

Oda, Masahiro. "Deep Learning in Surgical Assistance." Journal of Japan Society of Computer Aided Surgery 22, no. 1 (2020): 54–58. http://dx.doi.org/10.5759/jscas.22.54.

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2

Ellerman, David, Stephen Denning, and Nagy Hanna. "Active learning and development assistance." Journal of Knowledge Management 5, no. 2 (June 2001): 171–79. http://dx.doi.org/10.1108/13673270110393220.

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3

Riedl, Johannes, Daniel Puckmayr, and Dominik Brunner. "Tractor Assistance Systems Using Machine Learning." ATZheavy duty worldwide 13, no. 2 (June 2020): 50–55. http://dx.doi.org/10.1007/s41321-020-0079-6.

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4

McKeegan, Patricia. "Using Undergraduate Teaching Assistants in a Research Methodology Course." Teaching of Psychology 25, no. 1 (January 1998): 11–14. http://dx.doi.org/10.1207/s15328023top2501_4.

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In this article, I describe an undergraduate teaching assistant (TA.) program in which TAs engage in the complementary activities of course preparation, course instruction, and l-on-l tutorial assistance. Questionnaires completed by students enrolled in the beginning methodology course and from undergraduates serving as> TAs indicated that both groups viewed the program as a learning experience. Seventy-three percent of the TAs rated their work as an excellent learning experience. Over 91% of the students who worked with the teaching assistants rated the help they received as good to excellent.
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5

Bell, Diana Calhoun. "Learning Center Pedagogy and UDL." International Journal for Innovation Education and Research 3, no. 1 (January 31, 2015): 26–32. http://dx.doi.org/10.31686/ijier.vol3.iss1.298.

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In the United States, tutoring in higher education can trace its roots back as far as 1636 when Harvard, America’s first college, initially began educating the nation’s wealthy and elite students (Maxwell, 1997; Sheets 2011). These early forms of tutorials were based in remedial education; the goal was to bridge the gap between the level of education students brought to the institution and the level of education expected by that institution. Arendale (2010) provides a comprehensive review of the history of learning assistance in Access at the Crossroads: Learning Assistance in Higher Education. He provides a thorough and valuable six phase historical timeline, starting in the 1600’s through current forms of learning assistance programming (24). Early methods of learning assistance were created to enhance individual student performances in particular courses with which students struggled to succeed. This tutoring model persisted over time, becoming the archetypal form of learning assistance to improve student learning. However, Arendale’s research clearly shows that learning assistance has progressed by developing, expanding and increasing in both scope and complexity based on solid theory, research, and best practices.
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Shrivastava, Hitesh. "Symptomatic Assistance." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3658–60. http://dx.doi.org/10.22214/ijraset.2021.37132.

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The project aims to help the users get the idea if he/she may be suffering from heart disease or not. Web development is the work involved in developing a Website for the web (world wide web) or an intranet (a private network).Web development can range from Developing an easy single static page of plain text to complex web applications, electronic business and social networking services. The main goal of this website (SYMPTOMATIC ASSISTANCE) is to predict the possibility of having Heart disease. For this , the user needs to provide some information regarding their health. Such as blood pressure, glucose, cigarettes per day etc. According to which the website will respond. This will make people aware and help them improve their health. Machine learning is a method of data analysis that automates analytical model building. It is a branch of AI supported the thought that systems can learn from data, identify patterns and make decisions with minimal human intervention. For this we chose the best dataset from kaggle and used it in the best possible way to predict the output with high accuracy. For being able to predict the correct output, we applied a few machine learning models and chose the best fitted algorithm according to accuracy. For connecting machine Learning models with the webpages we used Flask. Flask is a micro framework written in Python. It is classified as a microframework because it doesn't require particular tools or libraries. It has no database abstraction layer, form validation, or any other components where pre-existing third-party libraries provide common functions. However, Flask supports extensions which will add application features as if they were implemented in Flask itself. Extensions exist for object-relational mappers, form validation, upload handling, various open authentication technologies and several common framework related tools.At the end will deploy our project using Heroku. Heroku is a cloud Platform as a service (PaaS) supporting several programming languages. One of the first cloud platforms, This project will make it easy for the user to know their health closely.
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Jennings, Jay, and Kasia Muldner. "Assistance that fades in improves learning better than assistance that fades out." Instructional Science 48, no. 4 (July 17, 2020): 371–94. http://dx.doi.org/10.1007/s11251-020-09520-7.

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8

Diniaty, Artina, and Lina Fauzi'ah. "Implementation of Learning Assistance to Chemistry Learning Motivation of Students in MAN 4 Sleman." International Journal of Chemistry Education Research 3, no. 1 (July 9, 2019): 49–52. http://dx.doi.org/10.20885/ijcer.vol3.iss1.art8.

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This study aims to determine the implementation of chemical learning assistance to students' learning motivation. Assistance efforts are carried out by managing the laboratory so that it can be used as a chemistry learning infrastructure, implementation, and discussion of tryout questions of SBMPTN along with the tricks to do them. In addition, chemedutainment is also carried out to motivate students to learn chemistry. The results showed that there were no significant differences in students' learning motivation through the implementation of chemical learning assistance. The implementation of chemical learning assistance needs to be carried out regularly and continuously in order to increase students' learning motivation.
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9

Begley, Gail S., Becca Berkey, Lisa Roe, and Hilary E. Y. Schuldt. "Becoming partners: Faculty come to appreciate undergraduates as teaching partners in a service-learning teaching assistant program." International Journal for Students as Partners 3, no. 1 (May 7, 2019): 89–105. http://dx.doi.org/10.15173/ijsap.v3i1.3669.

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This study examined the relationships between faculty and their teaching assistants in an undergraduate teaching assistant program developed at Northeastern University in the US to ease the challenges faculty faced in incorporating Service-Learning into their teaching. Feedback from faculty suggested that the undergraduates trained to assist them with purely logistical tasks were becoming partners in teaching. To explore the relationship between faculty and their teaching assistants and better understand how the faculty may have come to view the teaching assistants as partners, we conducted in-depth interviews with faculty across a range of academic disciplines and experience levels who had worked with one or more undergraduate teaching assistants. The data revealed that while the faculty participants did appreciate receiving logistical assistance with Service-Learning, they also benefited from partnering with students as colleagues who supported their teaching more broadly. We also found that faculty viewed the partnership in different ways depending on their level of experience with Service-Learning pedagogy.
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10

Duffy, Alex H. B., and Sandra M. Duffy. "Learning for design reuse." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10, no. 2 (April 1996): 139–42. http://dx.doi.org/10.1017/s0890060400001396.

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Over the past decade “design assistance,” that is, where the computer is viewed as an Intelligent Design Assistant (IDA) (MacCallum et al., 1987), has emerged in knowledge based design support and has formed the basic research strategy for the CAD Centre, University of Strathclyde, since the mid-1980s. Within this philosophy, an IDA would act as a colleague to a designer, providing guidance, learning from past design experiences, carrying out semi- and fully-automated tasks, explaining its reasoning and in essence complementing the designer's own natural skills, and thus leaving the ultimate decision-making, control, and responsibility with the designer (Fig. 1).
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11

De, Abir, Paramita Koley, Niloy Ganguly, and Manuel Gomez-Rodriguez. "Regression under Human Assistance." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 2611–20. http://dx.doi.org/10.1609/aaai.v34i03.5645.

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Decisions are increasingly taken by both humans and machine learning models. However, machine learning models are currently trained for full automation—they are not aware that some of the decisions may still be taken by humans. In this paper, we take a first step towards the development of machine learning models that are optimized to operate under different automation levels. More specifically, we first introduce the problem of ridge regression under human assistance and show that it is NP-hard. Then, we derive an alternative representation of the corresponding objective function as a difference of nondecreasing submodular functions. Building on this representation, we further show that the objective is nondecreasing and satisfies α-submodularity, a recently introduced notion of approximate submodularity. These properties allow a simple and efficient greedy algorithm to enjoy approximation guarantees at solving the problem. Experiments on synthetic and real-world data from two important applications—medical diagnosis and content moderation—demonstrate that the greedy algorithm beats several competitive baselines.
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12

MIWA, KAZUHISA, HITOSHI TERAI, MIKI MATSUMURO, and AKIHIRO MAEHIGASHI. "Assistance Dilemma : Giving and Withholding Learning Support." Japanese Journal of Educational Psychology 62, no. 2 (2012): 156–67. http://dx.doi.org/10.5926/jjep.62.156.

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13

Oda, Masahiro. "Artificial Intelligence/Deep Learning for Surgical Assistance." Journal of Japan Society of Computer Aided Surgery 21, no. 3 (2019): 143–46. http://dx.doi.org/10.5759/jscas.21.143.

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14

Omata, Toru, Masayuki Matsudaira, and Ken Inoue. "Step by Step Learning with Mechanical Assistance." Journal of the Robotics Society of Japan 16, no. 8 (1998): 1069–75. http://dx.doi.org/10.7210/jrsj.16.1069.

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15

Spitzer, Michael, Ibrahim Nanic, and Martin Ebner. "Distance Learning and Assistance Using Smart Glasses." Education Sciences 8, no. 1 (January 27, 2018): 21. http://dx.doi.org/10.3390/educsci8010021.

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With the everyday growth of technology, new possibilities arise to support activities of everyday life. In education and training, more and more digital learning materials are emerging, but there is still room for improvement. This research study describes the implementation of a smart glasses app and infrastructure to support distance learning with WebRTC. The instructor is connected to the learner by a video streaming session and gets the live video stream from the learner’s smart glasses from the learner’s point of view. Additionally, the instructor can draw on the video to add context-aware information. The drawings are immediately sent to the learner to support him to solve a task. The prototype has been qualitatively evaluated by a test user who performed a fine-motor-skills task and a maintenance task under assistance of the remote instructor.
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16

Pignat, Emmanuel, and Sylvain Calinon. "Learning adaptive dressing assistance from human demonstration." Robotics and Autonomous Systems 93 (July 2017): 61–75. http://dx.doi.org/10.1016/j.robot.2017.03.017.

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17

Hawrot, Anna. "Out-of-school learning assistance in adolescence." Educational Psychology 38, no. 4 (October 19, 2017): 513–34. http://dx.doi.org/10.1080/01443410.2017.1392006.

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18

Gowdy, E. Alana, and Shan A. Robertson. "POSTSECONDARY LEARNING ASSISTANCE: CHARACTERISTICS OF THE CLIENTELE." Community College Journal of Research and Practice 18, no. 1 (January 1994): 43–55. http://dx.doi.org/10.1080/1066892940180105.

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19

Morton, Arlene M. "Improving NCLEX Scores With Structured Learning Assistance." Nurse Educator 31, no. 4 (July 2006): 163–65. http://dx.doi.org/10.1097/00006223-200607000-00009.

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20

Pejatović, Aleksandra, and Edisa Kecap. "Providing employee assistance through multifunctional learning programmes." Andragoske studije, no. 2 (2018): 45–63. http://dx.doi.org/10.5937/andstud1802045p.

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21

Kaku, Ban, Tetsuya Matsumoto, Norimichi Kojo, and Guo Xin. "Learning assistance mechanism using case-based reasoning." Systems and Computers in Japan 29, no. 5 (May 1998): 73–83. http://dx.doi.org/10.1002/(sici)1520-684x(199805)29:5<73::aid-scj8>3.0.co;2-k.

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22

Harrington, Judy Stamos. "Multicultural Awareness Training for Learning Assistance Staff." Journal of College Reading and Learning 23, no. 2 (March 1991): 34–46. http://dx.doi.org/10.1080/10790195.1991.10849969.

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23

Van, Brinda. "College Learning Assistance Programs: Ingredients for Success." Journal of College Reading and Learning 24, no. 2 (March 1992): 27–39. http://dx.doi.org/10.1080/10790195.1992.10849987.

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24

Morton, Arlene M. "Improving NCLEX Scores With Structured Learning Assistance." Nurse Educator 33, Supplement (September 2008): 89S—91S. http://dx.doi.org/10.1097/01.ncn.0000336451.55036.03.

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25

Espejo-Garcia, Borja, Nikos Mylonas, Loukas Athanasakos, Spyros Fountas, and Ioannis Vasilakoglou. "Towards weeds identification assistance through transfer learning." Computers and Electronics in Agriculture 171 (April 2020): 105306. http://dx.doi.org/10.1016/j.compag.2020.105306.

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26

Sari, Nurazila, and Khamim Zarkasih Putro. "Assistance and Learning Strategies for Deaf Children." JOYCED: Journal of Early Childhood Education 1, no. 1 (June 19, 2021): 39–52. http://dx.doi.org/10.14421/joyced.2021.11-05.

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This study aimed to find out how the strategies for mentoring and learning deaf children are. The current phenomenon about deaf children is that they have difficulty communicating or can be categorized as low in communication with other people because their vocabulary is lacking. In the world of education, there are still few people who understand the characteristics of deaf children. It was also found that one of the teachers at an extraordinary school was more understanding when communicating with children with disabilities, mental disorders, and others than communicating with deaf children. This data collection technique is through descriptive analysis, content analysis or content analysis, and concluding a literary method with the type of library research, namely the mentoring and learning strategy for deaf children. There is an inclusive school for each child according to their particular needs. All efforts are made to be served optimally by making various modifications and adjustments. Learning media that can be used for deaf children are visual stimulation media and auditory stimulation media.
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27

Tran, Ngo My, Thach Keo Sa Rate, and Nguyen Ngoc Thao. "Effects of using E-learning system on learning outcomes of students in Can Tho University." Can Tho University Journal of Science 12, no. 3 (November 29, 2020): 27–37. http://dx.doi.org/10.22144/ctu.jen.2020.021.

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This study is conducted to assess the impact of using E-learning on students’ learning outcomes through combining models of technology acceptance model (TAM) and an information system success model (D&M model) basing on surveyed data from 294 students using E-learning system in Can Tho university. The main method used to evaluate this impact is structural equation model analysis method (SEM). The empirical results showed that perceived learning outcome is statistically influenced by three factors including learning assistance, community building assistance and perceived motivation. Of which, the community building assistance factor was found to play a strong role of students’ perceived learning outcome. Therefore, building solutions to develop of perceived motivation and community building assistance of the system in order to improve E-learning usage in Can Tho university should be paid attention in the coming time.
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Efendi, Yasin, Robinson Situmorang, and Diana Nomida Musnir. "How to Development Learning Models for Programing Algorithms E-Learning Assistance." Journal of Computational and Theoretical Nanoscience 17, no. 2 (February 1, 2020): 1523–33. http://dx.doi.org/10.1166/jctn.2020.8835.

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This research aims to develop a learning model of Programming Algorithms and the Data I Structure of e-learning assistance. Research and development of this model will produce products in the form of print modules, learning designs and e-learning assisted media for Algorithms and Data I Structure courses. Development of learning models for Programming Algorithms and Structure of Data I for e-learning assistance using the Dick and Carey model design modified with the Trollip and Alessi models. The steps taken in the development of this model are preliminary research, planning and model development, validation, evaluation and revision of the model, and implementation of the model. Validation results from experts consisting of 93% learning design experts, 95% material experts, and 87% of media experts recommend that this development model is used in learning Programming Algorithms and Structure of Data I. As for the implementation of individual trials are achieved 95% achievement, a small group trials reaching 95%, and a field test achieved 96% success. The conclusion of the expert validation process and the results of trials, that the development of learning models of Programming Algorithms and Structure of Data I for e-learning assistance is feasible to be used in learning.
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Otsuka, Sho, and Etsuko Harada. "Learning to use advanced driving assistance system: Aging effects on learning." Proceedings of the Annual Convention of the Japanese Psychological Association 79 (September 22, 2015): 3EV—069–3EV—069. http://dx.doi.org/10.4992/pacjpa.79.0_3ev-069.

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30

Indrašienė, Valdonė, Violeta Jegelevičienė, Odeta Merfeldaitė, and Asta Railienė. "Experience of Implementation of the Integrated Assistance Model in Providing Assistance to the Child." Pedagogika 129, no. 1 (April 25, 2018): 142–55. http://dx.doi.org/10.15823/p.2018.10.

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The article analyses the pedagogues’ experience in applying the model of integrated assistance to the child in school. The research involved 15 teachers responsible for coordination of the assistance to a specific child; the teachers represented three Lithuanian schools which have implemented the developed model. The pedagogues, as case coordinators, recorded the experience of implementation of the integrated assistance model in schools in their reflections. They’ve written down the reflections once per week in the form of a semi-structural electronic pedagogical diary. According to the analysis of the research data, in applying the integrated assistance model one of the major changes is the change in the learning motivation. The changes in learning motivation are related to changes in pupils’ learning outcomes, their involvement in the learning process, and school attendance. Pursuant to the research, self-esteem building is especially important for personality changes in children, and this ensured the increase of self-confidence. Improved relations between the child and his/her family and between the teachers and pupils were noticed in the relationship with the others. It is to be assumed that coordination of common actions of children, parents, teachers and other concerned assistance institutions had impact on the occurrence of these changes, as well as individualization of the assistance and anticipation of personal responsibility for a specific case management.
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31

Avhustiuk, Mariia M. "PSYCHOLOGICAL ASSISTANCE IN REDUCING ENGLISH LANGUAGE ANXIETY IN UNIVERSITY STUDENTS." Scientific Notes of Ostroh Academy National University: Psychology Series 1 (January 28, 2021): 26–34. http://dx.doi.org/10.25264/2415-7384-2021-12-26-34.

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The aim of the paper is to outline some possible ways of psychological assistance in defining and reducing foreign language anxiety in university students. In this context the analysis focuses on a theoretical study of language anxiety in the psychological literature and states its role in learning foreign languages and studying in university in general, highlights and explains some reasons of language anxiety, analyses its peculiarities and effect on the efficacy of learning foreign languages. Moreover, we aim at defining some possible ways of detection, prevention, and reducing foreign language anxiety trying to analyse some existent in psychological literature methods and practical questionnaires. To do this, we divide some practical methods available in the scientific psychological literature into the questionnaires aimed at detecting and preventing language anxiety (“Foreign Language Classroom Anxiety Scale” (FLCAS), “Language Learners’ Anxiety and Motivation”, “The Anxiety Scale”, “Test Anxiety Scale”, “Fear of Negative Evaluation”, etc.) and questionnaires that help reduce language anxiety (“Foreign Language Speaking Anxiety Scale”, “The Self-Regulated Foreign Language Learning Questionnaire”, “SWOT Analysis”, “Self-Regulation Questionnaire”, “Metacognitive Awareness Inventory”, “Promoting Student Metacogniton about Learning”, etc.). We also mention impact of self-regulated learning and importance of playing activities (games) during efficient learning, specify the role of fossilization. Practical implication of the paper is to provide the results of the students of the International Relations Department (N = 153) who voluntarily answered the questions of the adapted and translated into Ukrainian version of the “Foreign Language Classroom Anxiety Scale” (FLCAS). Our findings suggest that significant English language anxiety is experienced by many students. Finally, some implications for the next studies are proposed.
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32

Fern, A., S. Natarajan, K. Judah, and P. Tadepalli. "A Decision-Theoretic Model of Assistance." Journal of Artificial Intelligence Research 50 (May 20, 2014): 71–104. http://dx.doi.org/10.1613/jair.4213.

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There is a growing interest in intelligent assistants for a variety of applications from sorting email to helping people with disabilities to do their daily chores. In this paper, we formulate the problem of intelligent assistance in a decision-theoretic framework, and present both theoretical and empirical results. We first introduce a class of POMDPs called hidden-goal MDPs (HGMDPs), which formalizes the problem of interactively assisting an agent whose goal is hidden and whose actions are observable. In spite of its restricted nature, we show that optimal action selection for HGMDPs is PSPACE-complete even for deterministic dynamics. We then introduce a more restricted model called helper action MDPs (HAMDPs), which are sufficient for modeling many real-world problems. We show classes of HAMDPs for which efficient algorithms are possible. More interestingly, for general HAMDPs we show that a simple myopic policy achieves a near optimal regret, compared to an oracle assistant that knows the agent's goal. We then introduce more sophisticated versions of this policy for the general case of HGMDPs that we combine with a novel approach for quickly learning about the agent being assisted. We evaluate our approach in two game-like computer environments where human subjects perform tasks, and in a real-world domain of providing assistance during folder navigation in a computer desktop environment. The results show that in all three domains the framework results in an assistant that substantially reduces user effort with only modest computation.
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33

Malathi, T. "Advance Driver Assistance System using Machine Learning Technique." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 2018): 1966–75. http://dx.doi.org/10.22214/ijraset.2018.4336.

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34

Oestreich, Hendrik, Torben Töniges, Michael Wojtynek, and Sebastian Wrede. "Interactive Learning of Assembly Processes using Digital Assistance." Procedia Manufacturing 31 (2019): 14–19. http://dx.doi.org/10.1016/j.promfg.2019.03.003.

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Chen, Wei-Fan, Mei-Hua Chen, Ming-Lung Chen, and Lun-Wei Ku. "A Computer-Assistance Learning System for Emotional Wording." IEEE Transactions on Knowledge and Data Engineering 28, no. 5 (May 1, 2016): 1093–104. http://dx.doi.org/10.1109/tkde.2015.2507579.

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36

Simpson, W. R., and C. S. Dowling. "WRAPLE: The Weighted Repair Assistance Program Learning Extension." IEEE Design & Test of Computers 3, no. 2 (1986): 66–73. http://dx.doi.org/10.1109/mdt.1986.294918.

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37

Sapta, Andy, Abdul Hamid, and Edi Syahputra. "Assistance of Parents In The Learning At Home." Journal of Physics: Conference Series 1114 (November 2018): 012020. http://dx.doi.org/10.1088/1742-6596/1114/1/012020.

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38

Saifan, Ramzi R., Waleed Dweik, and Mohamad Abdel-Majeed. "A machine learning based deaf assistance digital system." Computer Applications in Engineering Education 26, no. 4 (April 20, 2018): 1008–19. http://dx.doi.org/10.1002/cae.21952.

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Ferrari, Linda Hill, Maxine McDonald, and Marie E. Provencio. "Learning Skills Assistance to Minority Students at Risk." Journal of College Reading and Learning 20, no. 1 (January 1987): 71–77. http://dx.doi.org/10.1080/10790195.1987.10849901.

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40

BALCAZAR, FABRICIO E., CHRISTOPHER B. KEYS, and JUANA GARATE-SERAFINI. "Learning to Recruit Assistance to Attain Transition Goals." Remedial and Special Education 16, no. 4 (July 1995): 237–46. http://dx.doi.org/10.1177/074193259501600407.

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This study evaluated a program to develop help-recruiting competencies as a strategy to facilitate the transition of adjudicated male youths with disabilities residing in a segregated institution. six adjudicated male youths with disabilities participated in the study. a multiple baseline design across behaviors was used to evaluate the youths' acquisition of help-recruiting skills. self-reported measures of the actions taken by the youths in the process of pursuing transition goals were also collected. youths' performance in role-play situations and generalization probes indicated consistent improvements in help-recruiting skills following training. the youths established a total of 17 transition goals at the beginning of the study, of which 11 were attained as planned, 4 were still in progress at the end of data collection, and 2 were dropped. participants also increased the size of their social support network and reported improved satisfaction with their social competencies. the limitations and benefits of developing help-recruiting competencies to facilitate attainment of transition goals among at-risk youths with disabilities are discussed.
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Franklin, Doug, and Bob Blankenberger. "Program Evaluation of Community College Learning Assistance Centers." Community College Review 44, no. 1 (October 18, 2015): 3–25. http://dx.doi.org/10.1177/0091552115609998.

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42

Schiaffino, Silvia, Patricio Garcia, and Analia Amandi. "eTeacher: Providing personalized assistance to e-learning students." Computers & Education 51, no. 4 (December 2008): 1744–54. http://dx.doi.org/10.1016/j.compedu.2008.05.008.

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43

Srivastava, Sangeeta, Ashwani Varshney, Supriya Katyal, Ravneet Kaur, and Vibha Gaur. "A smart learning assistance tool for inclusive education." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 11981–94. http://dx.doi.org/10.3233/jifs-210075.

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The government has established special schools to cater to the needs of children with disabilities but they are often segregated rather than receiving equitable opportunities. Artificial Intelligence has opened new ways to promote special education with advanced learning tools. These tools enable to adapt to a typical classroom set up for all the students with or without disabilities. To ensure social equity and the same classroom experience, a coherent solution is envisioned for inclusive education. This paper aims to propose a cost-effective and integrated Smart Learning Assistance (SLA) tool for Inclusive Education using Deep Learning and Computer Vision techniques. It comprises speech to text and sign language conversion for hearing impaired students, sign language to text conversion for speech impaired students, and Braille to text for communicating with visually impaired students. The tool assists differently-abled students to make use of various teaching-learning opportunities conferred to them and ensures convenient two-way communication with the instructor and peers in the classroom thus makes learning easier.
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Wijayanti, Arwendis, Narendradewi Kusumastuti, Diah, and Miftah. "TIPS AND TRICKS ASSISTANCE CHILDREN LEARNING FROM HOME." IJCE (Indonesian Journal of Community Engagement) 2, no. 1 (February 22, 2021): 32–36. http://dx.doi.org/10.37471/ijce.v2i1.225.

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Peran orang tua sangatlah penting diperlukan dalam proses pembelajaran anak selama dirumah. Selain itu orang tua berperan penting dalam memberikan edukasi kepada anak yang masih belum bisa memahami tentang pandemi yang sedang mewabah untuk tetap berdiam diri dirumah agar tidak tertukar dan menularkan wabah pandemi COVID-19 ini.Kegiatan pengabdian ini dilakukan dalam satu kali pertemuan dengan peserta sebanyak 214 orang yang terdiri dari mahasiswa PG PAUD, guru, orang tua dan umum. Kegiatan pengabdian dilaksanakan dalam bentuk webinar melalui aplikasi zoom meeting. Diharapkan kegiatan pengabdian kepada masyarakat ini mampu meningkatkan pemahaman dan pengetahuan para orang tua, guru dan mahasiswa dalam menerapkan tips dan trik pendampingan anak belajar dari rumah di saat pandemi COVID-19 saat ini.
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Borracci, Giuliana, Erica Gauthier, Jay Jennings, Kyle Sale, and Kasia Muldner. "The Effect of Assistance on Learning and Affect in an Algebra Tutor." Journal of Educational Computing Research 57, no. 8 (January 7, 2019): 2032–52. http://dx.doi.org/10.1177/0735633118822103.

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We investigated the impact of assistance on learning and affect during problem-solving activities with a computer tutor we built using the Cognitive Tutor Authoring Tools framework. The tutor delivered its primary form of assistance in the form of worked-out examples. We manipulated the level of assistance the examples in the tutor provided, by having similar problem-example pairs in one version of the tutor (high-assistance condition) and reduced similarity problem-example pairs in the other version (reduced-assistance condition). The reduced-assistance condition resulted in significantly higher learning, without increasing negative affect like frustration.
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Kavitha, Ganesan, and Lawrance Raj. "Educational Data Mining and Learning Analytics Educational Assistance for Teaching and Learning." International Journal of Computer & organization Trends 41, no. 1 (March 25, 2017): 21–25. http://dx.doi.org/10.14445/22492593/ijcot-v41p304.

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Pangastuti, Ratna, Fifi Pratiwi, Alma’atus Fahyuni, and Kammariyati Kammariyati. "Pengaruh Pendampingan Orangtua Terhadap Kemandirian dan Tanggung Jawab Anak Selama Belajar dari Rumah." JECED : Journal of Early Childhood Education and Development 2, no. 2 (December 31, 2020): 132–46. http://dx.doi.org/10.15642/jeced.v2i2.727.

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The problem in this study is how the influence of parental assistance on children's independence and responsibility while learning from home is seen from parental assistance at RA Al-Amien Kamal Bangkalan. This study aims to describe the effect of parental assistance on children's independence and responsibility while learning from home. Our research is using quantitative research. The population of this research is the parents of B3 grade children at RA Al - Amien. Learning in kindergarten or RA schools in this study uses online learning with the guidance of parents and teachers at school. Students do online learning and interact with teachers using the Whatsapp Group application. This study aims to determine the effect of parental assistance on children's independence and responsibility while learning from home. Respondents taken were parents of students in the Perumnas Kamal Bangkalan area. The results showed that parental assistance greatly influences the independence and responsibility of children while learning from home.
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Jatkauskienė, Birutė, and Modestas Nugaras. "HIGHER EDUCATION LECTURERS’ SUPPORT/ASSISTANCE IN STUDENT LEARNING: STUDY CASE OF LITHUANIAN HIGHER SCHOOLS." SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference 1 (May 26, 2017): 184. http://dx.doi.org/10.17770/sie2017vol1.2266.

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This article analyses a number of aspects of lecturers’ support/assistance in student learning. Empirical study reveals support/assistance, provided by lecturers, that is focused on: development of interpersonal relationships, informing, facilitation of learning/studies, regulation of relations, modelling of learning/study activities and behaviour, a vision of student learning, student activities. The above listed aspects of support/assistance might serve as a particular performance indicator for some of lecturers in this field, so as to provide a real and effective help and assure a quality of learning and studies.
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Ştefan, Mihaela, Claudiu Bunăiaşu, and Alexandru Strungă. "Academic learning–from control and systematic assistance to autonomy." Procedia - Social and Behavioral Sciences 33 (2012): 243–47. http://dx.doi.org/10.1016/j.sbspro.2012.01.120.

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Heuer, Herbert, and Jenna Lüttgen. "Robot assistance of motor learning: A neuro-cognitive perspective." Neuroscience & Biobehavioral Reviews 56 (September 2015): 222–40. http://dx.doi.org/10.1016/j.neubiorev.2015.07.005.

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