Искусственный интеллект для учебной аналитики и этапы педагогического проектирования: обзор решений

  • Елена Другова Национальный исследовательский университет «Высшая школа экономики» https://orcid.org/0000-0002-4373-4341
  • Ирина Журавлева Национальный исследовательский университет «Высшая школа экономики» https://orcid.org/0000-0002-0364-4819
  • Ульяна Захарова Национальный исследовательский университет «Высшая школа экономики» https://orcid.org/0000-0003-4262-3057
  • Валерия Сотникова Национальный исследовательский университет «Высшая школа экономики» https://orcid.org/0000-0002-6082-8083
  • Кристина Яковлева Томский государственный педагогический университет; Национальный исследовательский Томский государственный университет https://orcid.org/0000-0001-5695-5808
Ключевые слова: искусственный интеллект, учебная аналитика, педагогическое проектирование, ADDIE, рекомендательные системы, высшее образование

Аннотация

Методы искусственного интеллекта (ИИ) все чаще используются в исследованиях и разработках в области учебной аналитики (УА), призванной анализировать данные, накопленные в процессе обучения, с целью повышения его результативности. С этой же целью создаются модели педагогического проектирования. Наиболее широко распространена сегодня модель ADDIE, раскладывающая создание учебного курса на этапы. Пользователи и исследователи критикуют методы ИИ и УА за слабую связь с практикой преподавания, а педагогическое проектирование — за недостаток доказательности и измеримости. Проведен обзор литературы с целью продемонстрировать перспективы объединения этих трех областей знания и практики посредством анализа технологических решений для высшего образования, описанных в научных публикациях. В теоретической части рассмотрены понятие ИИ, технологии и методы ИИ, области применения ИИ в образовании, понятие УА, ее границы, виды и сферы применения УА, понятие и суть педагогического проектирования, а также модель ADDIE, на которую опирается практическая часть исследования. Итоговую выборку публикаций составили 43 статьи. Решения, описанные в них, соотнесены с задачами этапов педагогического проектирования учебных курсов и на этом основании систематизированы. Обнаружено, что наименьшее количество описанных в литературе решений относится к этапам анализа, дизайна и оценивания, больше статей соответствует этапу разработки, и наибольшее количество публикаций отражает решения, предназначенные для этапа применения курса. Такое распределение публикаций может объясняться разницей в доступности данных на разных этапах создания курса, а также слабой методической рефлексией преподавателей на этапе оценивания. Перспективы применения ИИ и УА в педагогическом проектировании связаны с дальнейшим развитием моделей, с переходом от экспериментов к массовой практике, а также с наращиванием компетенций преподавателей. Выводы и вопросы, прозвучавшие в статье, могут задать новую, педагогически ориентированную рамку обсуждения применения ИИ и анализа учебных данных в образовании.

Скачивания

Литература

Abdelhakim M.N.A., Shirmohammadi S. (2008) Improving Educational Multimedia Selection Process Using Group Decision Support Systems. International Journal of Advanced Media and Communication, vol. 2, no 2, pp. 174–190. https://doi.org/10.1504/IJAMC.2008.018507

Abu-Dalbouh H.M. (2021) Application of Decision Tree Algorithm for Predicting Students’ Performance via Online Learning during Coronavirus Pandemic. Journal of Theoretical and Applied Information Technology, vol. 99, no 19, pp. 4546–4556.

Afridi A.H. (2019) Transparency for Beyond-Accuracy Experiences: A Novel User Interface for Recommender Systems. Procedia Computer Science, no 151, pp. 335–344. https://doi.org/10.1016/j.procs.2019.04.047

Afridi A.H. (2018) Stakeholders Analysis for Serendipitous Recommenders System in Learning Environments. Procedia Computer Science, no 130, pp. 222–230. https://doi.org/10.1016/j.procs.2018.04.033

Aldowah H., Al-Samarraie H., Fauzy W.M. (2019) Educational Data Mining and Learning Analytics for 21st Century Higher Education: A Review and Synthesis. Telematics and Informatics, vol. 37, April, pp. 13–49. https://doi.org/10.1016/j.tele.2019.01.007

Anaya A.R., Luque M., Peinado M. (2016) A Visual Recommender Tool in a Collaborative Learning Experience. Expert Systems with Applications, vol. 45, October, pp. 248–259. https://doi.org/10.1016/j.eswa.2015.01.071

Asli M. F., Hamzah M., Ibrahim A.A.A., Ayub E. (2020) Problem Characterization for Visual Analytics in MOOC Learner's Support Monitoring: A Case of Malaysian MOOC. Heliyon, vol. 6, no 12, Article no e05733. https://doi.org/10.1016/j.heliyon.2020.e05733

Baker R.S. (2021) Artificial Intelligence in Education: Bringing It All Together. OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. Paris: OECD, pp. 43–56. https://doi.org/10.1787/589b283f-en

Baker R.S., Inventado P.S. (2014) Educational Data Mining and Learning Analytics. Learning Analytics (eds J. Larusson, B. White), New York, NY: Springer, pp. 61–75. https://doi.org/10.1007/978-1-4614-3305-7_4

Baker R.S.J.D., Yacef K. (2009) The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, vol. 1, no 1, pp. 3–17. https://doi.org/10.5281/zenodo.3554657

Branch R.M. (2009) Instructional Design: The ADDIE Approach. New York NY: Springer Science & Business Media. https://doi.org/10.1007/978-0-387-09506-6

Cabrera I., Villalon J. (2013) An Adaptive Interface for Computer-Assisted Rubrics in an E-Marking Tool Using Nearest Neighbor. Paper presented at International Conference on Advanced Learning Technologies (Beijing, China, 2013, 15–18 July) https://doi.org/10.1109/ICALT.2013.27

Carchiolo V., Longheu A., Malgeri M. (2010) Reliable Peers and Useful Resources: Searching for the Best Personalised Learning Path in a Trust- and Recommendation-Aware Environment. Information Sciences, vol. 180, no 10, pp. 1893–1907. https://doi.org/10.1016/j.ins.2009.12.023

Chan P., van Gerven T., Dubois J-L., Bernaerts K. (2021) Virtual Chemical Laboratories: A Systematic Literature Review of Research, Technologies and Instructional Design. Computers and Education Open, vol. 2, December, Article no 100053. https://doi.org/10.1016/j.caeo.2021.100053

Chassignol M., Khoroshavin A., Klimova A., Bilyatdinova A. (2018) Artificial Intelligence Trends in Education: A Narrative Overview. Procedia Computer Science, vol. 136, January, pp. 16–24. https://doi.org/10.1016/j.procs.2018.08.233

Chen Y.-C., Chang Y.-S., Chuang M.-J. (2022) Virtual Reality Application Influences Cognitive Load-Mediated Creativity Components and Creative Performance in Engineering Design. Journal of Computer Assisted Learning, vol. 38, no 1, pp. 6–18. https://doi.org/10.1111/jcal.12588

Chen Z., Xu M., Hu Z., Zhang S., Zhang J., Jiang X., Jumani A.K. (2021) Multimedia Educational System and Its Improvement Using AI Model for a Higher Education Platform. Journal of Multiple-Valued Logic and Soft Computing, vol. 36, no 1, pp. 25–41.

Clow D. (2013) An Overview of Learning Analytics. Teaching in Higher Education, vol. 18, no 6, pp. 683–695. https://doi.org/10.1080/13562517.2013.827653

Cobos С., Rodriguez O., Rivera J., Betancourt J., Mendoza M., Leon E., Herrera-Viedma E. (2013) A Hybrid System of Pedagogical Pattern Recommendations Based on Singular Value Decomposition and Variable Data Attributes. Information Processing & Management, vol. 49, no 3, pp. 607–625. https://doi.org/10.1016/j.ipm.2012.12.002

Conole G. (2012) Designing for Learning in an Open World. New York NY: Springer Science & Business Media. https://doi.org/10.1007/978-1-4419-8517-0

Corrin L., Kennedy G., de Barba P.G., Lockyer L. et al. (2016) Completing the Loop: Returning Meaningful Learning Analytic Data to Teachers. A Handbook for Educators and Learning Analytics Specialists. Sydney, NSW, Australia: Government of Australia Office for Learning and Teaching.

Cung B., Xu D., Eichhorn S., Warschauer M. (2019) Getting Academically Underprepared Students Ready through College Developmental Education: Does the Course Delivery Format Matter? American Journal of Distance Education, vol. 33, no 4, pp. 178–194. https://doi.org/10.1080/08923647.2019.1582404

De Medio C., Limongelli C., Sciarrone F., Temperini M. (2020) MoodleREC: A Recommendation System for Creating Courses Using the Moodle e-Learning Platform. Computers in Human Behavior, Vol. 104, Article no 106168. https://doi.org/10.1016/j.chb.2019.106168

Deng L., Yu D. (2014) Deep Learning: Methods and Applications. Foundations and Trends in Signal Processing, vol. 7, no 3-4, pp. 197–387. https://doi.org/10.1561/2000000039

Deo R.C., Yaseen Z.M., Al-Ansari N., Nguyen-Huy T., Mcpherson Langlands T.A, Galligan L. (2020) Modern Artificial Intelligence Model Development for Undergraduate Student Performance Prediction: An Investigation on Engineering Mathematics Courses. IEEE Access, vol. 8, July, pp. 136697–136724. https://doi.org/10.1109/ACCESS.2020.3010938

Dias S.B., Hadjileontiadou S.J., Hadjileontiadis L.J., Diniz J.A. (2015) Fuzzy Cognitive Mapping of LMS Users’ Quality of Interaction within Higher Education Blended-Learning Environment. Expert Systems with Applications, vol. 42, iss. 21, pp. 7399–7423. https://doi.org/10.1016/j.eswa.2015.05.048

Drljača D., Latinović B., Stanković Z., Cvetković D. (2017) ADDIE Model for Development of E-Courses. Proceedings of the Sinteza 2017 — International Scientific Conference on Information Technology and Data Related Research (Belgrade, 2017, 21 April), pp. 242–247. https://doi.org/10.15308/Sinteza-2017-242-247

Doleck T., Lemay D.J., Basnet R.B., Bazelais P. (2019) Predictive Analytics in Education: A Comparison of Deep Learning Frameworks. Education and Information Technologies, vol. 25, no 3. https://doi.org/10.1007/s10639-019-10068-4

Duggal K., Gupta L.R., Singh P. (2021) Gamification and Machine Learning Inspired Approach for Classroom Engagement and Learning. Mathematical Problems in Engineering, vol. 2021, Article ID 9922775. https://doi.org/10.1155/2021/9922775

Edwards B.I., Cheok A.D. (2018) Why Not Robot Teachers: Artificial Intelligence for Addressing Teacher Shortage. Applied Artificial Intelligence, vol. 32, no 4, pp. 345–360. https://doi.org/10.1080/08839514.2018.1464286

Ellis R.A., Goodyear P. (2010) Students' Experiences of e-Learning in Higher Education: The Ecology of Sustainable Innovation. New York NY: Routledge.

Fiallos A., Ochoa X. (2019) Semi-Automatic Generation of Intelligent Curricula to Facilitate Learning Analytics. Proceedings of the 9th International Conference on Learning Analytics & Knowledge (Tempe, Arizona, 2019, 04–08 March), pp. 46–50. https://doi.org/10.1145/3303772.3303834

Fidan M., Gencel N. (2022) Supporting the Instructional Videos with Chatbot and Peer Feedback Mechanisms in Online Learning: The Effects on Learning Performance and Intrinsic Motivation. Journal of Educational Computing Research, vol. 60, no 6, Article no 073563312210779. https://doi.org/10.1177/07356331221077901

Fosch-Villaronga E., Lutz C., Tamò-Larrieux A. (2020) Gathering Expert Opinions for Social Robots’ Ethical, Legal, and Societal Concerns. International Journal of Social Robotics, vol. 12, no 2, pp. 441–458. https://doi.org/10.1007/s12369-019-00605-z

García E., Romero C., Ventura S., De Castro C. (2011) A Collaborative Educational Association Rule Mining Tool. The Internet and Higher Education, vol. 14, no 2, pp. 77–88. https://doi.org/10.1016/j.iheduc.2010.07.006

Gardner J., O'Leary M., Yuan L. (2021) Artificial Intelligence in Educational Assessment: ‘Breakthrough? Or Buncombe and Ballyhoo? Journal of Computer Assisted Learning, vol. 37, no 5, pp. 1207–1216. https://doi.org/10.1111/jcal.12577

Garg S., Sharma S. (2020) Impact of Artificial Intelligence in Special Need Education to Promote Inclusive Pedagogy. International Journal of Information and Education Technology, vol. 10, no 7, pp. 523–527. https://doi.org/10.18178/ijiet.2020.10.7.1418

George G., Lal A.M. (2019) Review of Ontology-Based Recommender Systems in e-Learning. Computers & Education, vol. 142, December, Article no 103642. https://doi.org/10.1016/j.compedu.2019.103642

Goksel N., Bozkurt A. (2019) Artificial Intelligence in Education: Current Insights and Future Perspectives. Handbook of Research on Learning in the Age of Transhumanism (eds S. Sisman-Ugur, G. Kurubacak), Hershey PA: IGI Global, pp. 224–236. https://doi.org/10.4018/978-1-5225-8431-5.ch014

Guan C., Mou J., Jiang Z. (2020) Artificial Intelligence Innovation in Education: A Twenty-Year Data-Driven Historical Analysis. International Journal of Innovation Studies, vol. 4, no 4, pp. 134–147. https://doi.org/10.1016/j.ijis.2020.09.001

Guruge D.B., Kadel R., Halder S.J. (2021) The State of the Art in Methodologies of Course Recommender Systems — A Review of Recent Research. Data, no 6, Article no 18. https://doi.org/10.3390/data6020018

Hamam D. (2021) The New Teacher Assistant: A Review of Chatbots’ Use in Higher Education. Proceedings of the 23rd HCI International Conference, HCII 2021 (Virtual Event, 2021, 24–29 July), part III, pp. 59–63. https://doi.org/10.1007/978-3-030-78645-8_8

Harrathi M., Braham R. (2021) Recommenders in Improving Students’ Engagement in Large Scale Open Learning. Procedia Computer Science, vol. 192, no 1, pp. 1121–1131. https://doi.org/10.1016/j.procs.2021.08.115

Hasanov A., Laine T.H., Chung T.-S. (2019) A Survey of Adaptive Context-Aware Learning Environments. Journal of Ambient Intelligence and Smart Environments, vol. 11, no 5, pp. 403–428. https://doi.org/10.3233/AIS-190534

Herodotou C., Rienties B., Boroowa A., Zdrahal Z., Hlosta M. (2019) A Large-Scale Implementation of Predictive Learning Analytics in Higher Education: The Teachers` Role and Perspective. Educational Technology Research and Development, vol. 67, no 2, pp. 1273–1306. https://doi.org/10.1007/s11423-019-09685-0

Herranz S.M., Palomo J., del Carmen de la Orden de la Cruz M. (2018) Building an Educational Platform Using NLP: A Case Study in Teaching Finance. Journal of Universal Computer Science, vol. 24, no 10, pp. 1403–1423.

Hooda M., Rana C., Dahiya O., Rizwan A., Hossain M.S. (2022) Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education. Mathematical Problems in Engineering, vol. 2022, Article ID 5215722. https://doi.org/10.1155/2022/5215722

Jiang L. (2021) Virtual Reality Action Interactive Teaching Artificial Intelligence Education System. Complexity, vol. 2021, Article ID 5553211. https://doi.org/10.1155/2021/5553211

Jokhan A., Chand A.A., Singh V., Mamun K.A. (2022) Increased Digital Resource Consumption in Higher Educational Institutions and the Artificial Intelligence Role in Informing Decisions Related to Student Performance. Sustainability, no.14. pp. 4–13.

Joveliano D.A., Galli I.M., Dos Santos Júnior G.N., da Silva M.R.A., Benites C.D.S., Ribeiro F.C. (2020) Working with a Hearing Disability: A Proposal for Distance Teaching with Chabot. RISTI — Revista Iberica de Sistemas e Tecnologias de Informacao / Iberian Journal of Information Systems and Technologies, no E29, pp. 135–147.

Kabudi T., Pappas I., Olsen D.H. (2021) AI-Enabled Adaptive Learning Systems: A Systematic Mapping of the Literature. Computers & Education: Artificial Intelligence (CAEAI), vol. 2, Article no 100017. https://doi.org/10.1016/j.caeai.2021.100017

Kizilkaya L., Vince D., Holmes W. (2019) Design Prompts for Virtual Reality in Education. Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science (eds S. Isotan, E. Millán, A. Ogan, P. Hastings, B. McLaren, R. Luckin), vol. 11626. Cham: Springe. https://doi.org/10.1007/978-3-030-23207-8_25

Kwon C. (2018) A Study on the Relationship of Distraction Factors, Presence, Flow, and Learning Effects in HMD-based Immersed VR Learning. Journal of Korea Multimedia Society, vol. 21, no 8, pp. 1002–1020. https://doi.org/10.9717/KMMS.2018.21.8.1002

Laal M., Ghodsi S.M. (2012) Benefits of Collaborative Learning. Procedia—Social and Behavioral Sciences, no 31, pp. 486–490. https://doi.org/10.1016/j.sbspro.2011.12.091

Larrabee Sønderlund A., Hughes E., Smith J. (2019) The Efficacy of Learning Analytics Interventions in Higher Education: A Systematic Review. British Journal of Educational Technology, vol. 50, no 5, pp. 2594–2618. https://doi.org/10.1111/bjet.12720

Leaton Gray S. (2020) Artificial Intelligence in Schools: Towards a Democratic Future. London Review of Education, vol. 18, no 2, pp. 163–177. https://doi.org/10.14324/LRE.18.2.02

Leeuwen van A., Janssen J., Erkens G., Brekelmans M. (2014) Supporting Teachers in Guiding Collaborating Students: Effects of Learning Analytics in CSCL. Computers & Education, vol. 79, October, pp. 28–39. https://doi.org/10.1016/j.compedu.2014.07.007

Leitner P., Khalil M., Ebner M. (2017) Learning Analytics in Higher Education—A Literature Review. Learning Analytics: Fundaments, Applications, and Trends (ed. A. Peña-Ayala), Cham, Switzerland: Springer, pp. 1–23. https://doi.org/10.1007/978-3-319-52977-6_1

Lim L.-A., Gentili S., Pardo A., Kovanović V., Whitelock-Wainwright A., Gašević D., Dawson S. (2021) What Changes, and for Whom? A Study of the Impact of Learning Analytics-Based Process Feedback in a Large Course. Learning and Instruction, vol. 72, Article no 101202. https://doi.org/10.1016/j.learninstruc.2019.04.003

Luckin R., Holmes W., Griffiths M., Forcier L.B. (2016) Intelligence Unleashed: An Argument for AI in Education. London: Pearson.

Lutz C., Schöttler M., Hofmann C. (2019) The Privacy Implications of Social Robots. Mobile Media & Communication, vol. 7, no 3, pp. 412–434. https://doi.org/10.1177/2050157919843961

Maitra S., Madan S., Kandwal R., Mahajan P. (2018) Mining Authentic Student Feedback for Faculty Using Naïve Bayes Classifier. Procedia—Computer Science, vol. 132, pp. 1171–1183. https://doi.org/10.1016/j.procs.2018.05.032

Mangaroska K., Giannakos M. (2019) Learning Analytics for Learning Design: A Systematic Literature Review of Analytics-Driven Design to Enhance Learning. IEEE Transactions on Learning Technologies, vol. 12, no 4, pp. 516–534. https://doi.org/10.1109/TLT.2018.2868673

Martinho V.R.C., Nunes C., Minussi C.R. (2013) An Intelligent System for Prediction of School Dropout Risk Group in Higher Education Classroom Based on Artificial Neural Networks. Proceedings of the 25th IEEE International Conference on Tools with Artificial Intelligence. ICTAI 2013 (Herndon, VA, 2013, 04–06 November), pp. 159–166. https://doi.org/10.1109/ICTAI.2013.33

McCarthy J., Minsky M.L., Rochester N., Shannon C.E. (2006) A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955. AI Magazine, vol. 27, no 4, pp. 12–14. https://doi.org/10.1609/aimag.v27i4.1904

Mihailescu M.I., Nita S.L., Pau V.C. (2016) New Big Data Model Based on Social Learning Environment Using Artificial Intelligence. E-learning Vision 2020! Conference Proceedings of ˮeLearning and Software for Educationˮ (eLSE), vol. 1, no 12, pp. 428–435.

Mirchi N., Bissonnette V., Yilmaz R., Ledwos N., Winkler-Schwartz A., Del Maestro R. (2020) The Virtual Operative Assistant: An Explainable Artificial Intelligence Tool for Simulation-Based Training in Surgery and Medicine. Plos One, vol. 15, no 2, Article no e0229596. https://doi.org/10.1371/journal.pone.0229596

Mohamad S.K., Tasir Z. (2013) Educational Data Mining: A Review. Procedia—Social and Behavioral Sciences, vol. 97, pp. 320–324. https://doi.org/10.1016/j.sbspro.2013.10.240

Montalvo S., Palomo J., de la Orden C. (2018) Building an Educational Platform Using NLP: A Case Study in Teaching Finance. Journal of Universal Computer Science, vol. 24, no 10, pp. 1403–1423.

Nkhoma C., Dang -Pham D., Hoang A.P., Nkhoma M., Le-Hoai T., Thomas S. (2019) Learning Analytics Techniques and Visualisation with Textual Data for Determining Causes of Academic Failure. Behaviour & Information Technology, vol. 39, no 9, pp. 808–823. https://doi.org/10.1080/0144929X.2019.1617349

Nunn S., Avella J., Kanai T., Kebritchi M. (2016) Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review. Online Learning Journal, vol. 20, no 2, pp. 1–17. https://doi.org/10.24059/olj.v20i2.790

Ognjanovic I., Gasevic D., Dawson S. (2016) Using Institutional Data to Predict Student Course Selections in Higher Education. The Internet and Higher Education, vol. 29, no 2, pp. 49–62. https://doi.org/10.1016/j.iheduc.2015.12.002

Ong V.K. (2016) Business Intelligence and Big Data Analytics for Higher Education: Cases from UK Higher Education Institutions. Information Engineering Express, vol. 2, no 1, pp. 65–75. https://doi.org/10.52731/iee.v2.i1.63

París-Requeiro M.T., Cabrero-Canosa M.J. (2010) Personalized Construction of Self-Evaluation Tests. 2010 IEEE Education Engineering Conference, EDUCON 2010 (Madrid, Spain, 2010, 14–16 April), pp. 863–868. https://doi.org/10.1109/EDUCON.2010.5492486

Pelletier K., McCormack M., Reeves J., Robert J., Arbino N., Grajek S. (2021) 2021 EDUCAUSE Horizon Report. Teaching and Learning Edition. Boulder, CO: EDUCAUSE.

Popenici S.A.D., Kerr S. (2017) Exploring the Impact of Artificial Intelligence on Teaching and Learning in Higher Education. Research and Practice in Technology Enhanced Learning, vol. 12, Article no 22. https://doi.org/10.1186/s41039-017-0062-8

Qureshi M.A., Khaskheli A., Qureshi J.A., Raza S.A., Yousufi S.Q. (2021) Factors Affecting Students’ Learning Performance through Collaborative Learning and Engagement. Interactive Learning Environments. https://doi.org/10.1080/10494820.2021.1884886

Raju A., Nair M., Nair A., Seenivasan R. (2018) Hybrid Learning Environment: Learning Mathematics Using ALEKS Software. International Conference on e-Learning P. 336–343.

Reiser R.A., Dempsey J.V. (eds) (2007) Trends and Issues in Instructional Design and Technology. Upper Saddle River NJ: Pearson.

Rienties B., Køhler Simonsen H., Herodotou C. (2020) Defining the Boundaries between Artificial Intelligence in Education, Computer-Supported Collaborative Learning, Educational Data Mining, and Learning Analytics: A Need for Coherence. Frontiers in Education, vol. 5, July, Article no 128. https://doi.org/10.3389/feduc.2020.00128

Rincón-Flores E.G., Lopez-Camacho E., Mena J., Lopez O.O. (2020) Predicting Academic Performance with Artificial Intelligence (AI), a New Tool for Teachers and Students. Proceedings of the 11th IEEE Global Engineering Education Conference, EDUCON 2020 (Porto, Portugal, 2020, 27–30 April), pp. 1049–1054. https://doi.org/10.1109/EDUCON45650.2020.9125141

Romero C., Ventura S. (2010) Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 40, no 6, pp. 601–618. https://doi.org/10.1109/TSMCC.2010.2053532

Romero C., Ventura S. (2007) Educational Data Mining: A Survey from 1995 to 2005. Expert Systems with Applications, vol. 33, no 1, pp. 135–146. https://doi.org/10.1016/j.eswa.2006.04.005

Ruan S., Jiang L., Xu Q., Liu Zh., Davis G. M., Brunskil E.l, Landay J. A. (2021) EnglishBot: An AI-Powered Conversational System for Second Language Learning. Proceedings of the 26th International Conference on Intelligent User Interfaces (IUI '21) (Virtually hosted by Texas A&M University, 2021, 13–17 April), pp. 434–444. https://doi.org/10.1145/3397481.3450648

Seel N. M., Lehmann T., Blumschein P., Podolskiy O. A. (2017) Instructional Design for Learning: Theoretical Foundations. Rotterdam, NL: Sense. https://doi.org/10.1007/978-94-6300-941-6

Sergis S., Sampson D.G. (2017) Teaching and Learning Analytics to Support Teacher Inquiry: A Systematic Literature Review. Learning Analytics: Fundaments, Applications, and Trends. Studies in Systems, Decision and Control (ed. A. Peña-Ayala), Cham, Switzerland: Springer, pp. 25–63. https://doi.org/10.1007/978-3-319-52977-6_2

Shum S.B., Ferguson R. (2012) Social Learning Analytics. Journal of Educational Technology & Society, vol. 15, no 3, pp. 3–26.

Siemens G., Baker R.S.D. (2012) Learning Analytics and Educational Data Mining: Towards Communication and Collaboration. Proceedings of the 2nd international Conference on Learning Analytics and Knowledge (Vancouver, BC, 2012, 29 April — 2 May), pp. 252–254. https://doi.org/10.1145/2330601.2330661

Siemens G., Long P. (2011) Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, vol. 46, no 5, P. 31–40.

Sohail S., Alvi A., Khanum A. (2022) Interpretable and Adaptable Early Warning Learning Analytics Model. CMC—Computers Materials & Continua, vol. 71, no 2, pp. 3211–3225. https://doi.org/10.32604/cmc.2022.023560

Stoica A. S., Heras S., Palanca J., Julián V., Mihaescu M. C. (2021) Classification of Educational Videos by Using a Semi-Supervised Learning Method on Transcripts and Keywords. Neurocomputing, vol. 456, October, pp. 637–647. https://doi.org/10.1016/j.neucom.2020.11.075

Suganya G., Premalatha M., Dubey P., Drolia A.R., Srihari S.N. (2020) Subjective Areas of Improvement: A Personalized Recommendation. Procedia—Computer Science, vol. 172, pp. 235–239. https://doi.org/10.1016/j.procs.2020.05.037

Syed T.A., Palade V., Iqbal R., Nair, S.S. (2017) A Personalized Learning Recommendation System Architecture for Learning Management System. Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR 2017) (Funchal, Madeira, Portugal, 2017, 01–03 November), pp. 275–282. https://doi.org/10.5220/0006513202750282

Tamayo P., Herrero A., Martín J.S., Navarro C., Tránchez, J.M. (2020) Design of a Chatbot as a Distance Learning Assistant. Open Praxis, vol. 12, no 1, pp. 145–153. https://doi.org/10.5944/openpraxis.12.1.1063

Thai-Nghe N., Drumond L., Krohn-Grimberghe A., Schmidt-Thieme L. (2010) Recommender System for Predicting Student Performance. Procedia—Computer Science, vol.1, pp. 2811–2819. https://doi.org/10.1016/j.procs.2010.08.006

Tsai Y. S., Gasevic D. (2017) Learning Analytics in Higher Education—Challenges and Policies: A Review of Eight Learning Analytics Policies. Proceedings of the Seventh International Learning Analytics & Knowledge Conference (Vancouver, BC, 2017, 13–17 March), pp. 233–242. https://doi.org/10.1145/3027385.3027400

Turhan M., Erol Y.C., Ekici, S. (2016) Predicting Students’ School Engagement Using Artificial Neural Networks. International Journal of Advances in Science, Engineering and Technology, vol. 4, iss. 2, pp. 159–62.

Variawa C., McCahan S. (2014) Engineering Vocabulary Development Using an Automated Software Tool. 121st ASEE Annual Conference (Indianapolis, IN, 2014, 15–18 June), Paper ID #8663.

Verbert K., Ochoa X., Derntl M., Wolpers M., Pardo A., Duval E. (2012) Semi-Automatic Assembly of Learning Resources. Computers & Education, vol. 59, no 4, pp. 1257–1272. https://doi.org/10.1016/j.compedu.2012.06.005

Yan H., Lin F. (2021) Including Learning Analytics in the Loop of Self-Paced Online Course Learning Design. International Journal of Artificial Intelligence in Education, vol. 31, no 4, pp. 878–895. https://doi.org/10.1007/s40593-020-00225-z

Zapata A., Domínguez V., Prieto M., Romero C. (2013) A Framework for Recommendation in Learning Object Repositories: An Example of Application in Civil Engineering. Advances in Engineering Software, vol. 56, February, pp. 1–14. https://doi.org/10.1016/j.advengsoft.2012.10.005

Zapata A., Menedoz V., Prieto M., Romero C. (2015) Evaluation and Selection of Group Recommendation Strategies for Collaborative Searching of Learning Objects. International Journal of Human-Computer Studies, vol. 76, April, pp. 22–39. https://doi.org/10.1016/j.ijhcs.2014.12.002

Zawacki-Richter O., Marin V. I., Bond M., Gouverneur F. (2019) Systematic Review of Research on Artificial Intelligence Applications in Higher Education—Where Are the Educators? International Journal of Educational Technology in Higher Education, vol. 16, no 1, pp. 27. https://doi.org/10.1186/s41239-019-0171-0

Zhang X., Cao Z. (2021) A Framework of an Intelligent Education System for Higher Education Based on Deep Learning. International Journal of Emerging Technologies in Learning vol. 16, no 7, pp. 233–248. https://doi.org/10.3991/ijet.v16i07.22123

Zotou M., Tambouris E., Tarabanis K. (2020) Data-Driven Problem Based Learning: Enhancing Problem Based Learning with Learning Analytics. Educational Technology Research and Development, vol. 68, no 6, pp. 3393–3424. https://doi.org/10.1007/s11423-020-09828-8

Опубликован
2022-12-22
Как цитировать
Другова, Елена, Ирина Журавлева, Ульяна Захарова, Валерия Сотникова, и Кристина Яковлева. 2022. «Искусственный интеллект для учебной аналитики и этапы педагогического проектирования: обзор решений». Вопросы образования / Educational Studies Moscow, вып. 4 (декабрь), 107-53. https://doi.org/10.17323/1814-9545-2022-4-107-153.
Выпуск
Раздел
Исследовательские статьи
Plum Print visual indicator of research metrics
  • Citations
    • Citation Indexes: 6
  • Captures
    • Readers: 78
see details

Наиболее читаемые статьи этого автора (авторов)