فصلنامه روندها و دستاوردها در فناوری یادگیری

فصلنامه روندها و دستاوردها در فناوری یادگیری

مرور نظام‏مند و ارائه مدل مفهومی سنجش شخصی‌سازی‌شده مبتنی بر وب معنایی

نوع مقاله : مقاله پژوهشی

نویسندگان
دانشگاه علامه طباطبایی
چکیده
نیاز روزافزون به شخصی‏سازی در فرایند سنجش و توجه به ویژگی‏های فردی فراگیران در دنیای امروز، نظر پژوهشگران را بیش‌ازپیش به خود جلب کرده است. با بهره‏گیری از سنجش شخصی‌سازی‌شده، فراگیران با آزمونی مواجه می‏شوند که به‌طور خاص برای رفع نیازهای آن‏ها طراحی شده است، تا فرصتی برای ارتقاء توانمندی در زمینه‏هایی که ممکن است در ابتدا با چالش مواجه باشند فراهم گردد. این مطالعه با هدف تبیین مبانی نظری و پژوهشی سنجش شخصی‌سازی‌شده یادگیری فراگیران با استفاده از امکانات وب معنایی انجام شد. پژوهش حاضر از نوع کیفی و به روش استقرایی، به شیوه مرور سیستماتیک به شرح زیر انجام شده است: الف) تدوین مسئله، ب) جمع‌آوری داده‏ها، ج) ارزیابی مناسب بودن داده‏ها، د) تجزیه‌وتحلیل و تفسیر داده‏های مربوطه و ه) سازمان‌دهی و ارائه نتایج بر اساس فرآیند انتخاب مطالعه. درنهایت 30 منبع برای تجزیه‌وتحلیل انتخاب شدند و یک چارچوب کلی برای طراحی سیستم و نیز مدل مفهومی سناریوی سنجش شخصی‌سازی‌شده مبتنی بر وب معنایی طراحی شد. یافته‏ها نشان داد استفاده از قواعد وب معنایی می‏تواند در رفع چالش شخصی‏سازی در سنجش مؤثر واقع شود. به‌طوری‌که با شناسایی خلأهای یادگیری هر فراگیر، استفاده از شکل بصری بازخورد، تعیین مسیر یادگیری شخصی در محیطی تعاملی و مشارکت در گروه‏های طبقه‌بندی‌شده بر اساس شباهت‏هایشان، فراگیران را برای تحقق اهداف یادگیری و بهبود عملکرد پایدار هدایت نماید. از طرفی میزان دسترسی و حمایت مربیان و امنیت داده‏ها از چالش‏های پیش رو برای معلمان و فراگیران است.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

A Systematic Review and Presentation of a Conceptual Model of Personalized Assessment Based on the Semantic Web

نویسندگان English

Akram Khosrogerdi
Mohammad Reza Nili Ahmadabadi
Allameh Tabataba'i University
چکیده English

The increasing need for personalization in the assessment process, focusing on individual learners’ characteristics, has captured researchers’ attention more than ever in today’s world. By employing personalized measurement, learners encounter assessments specifically designed to address their needs, providing an opportunity to enhance their abilities in areas where they may initially face challenges. This study aimed to elucidate the theoretical and research foundations of personalized assessment using semantic web capabilities. The qualitative research was conducted through an inductive approach, involving the following steps: a) problem formulation, b) data collection, c) evaluation of data appropriateness, d) data analysis and interpretation, and e) organization and presentation of results based on the study selection process. Ultimately, 30 articles were selected for analysis, leading to a general framework for designing a system and a conceptual model for a semantically web-based personalized assessment scenario. The findings showed that the use of semantic web rules can be effective in solving the challenge of personalization in assessment. So that by identifying the learning gaps of each learner, using the visual form of feedback, determining the path of personal learning in an interactive environment and participating in groups classified according to their similarities, guiding learners to achieve learning goals and improve sustainable performance. To do, On the other hand, the level of access and support of instructors and data security are challenges for teachers and learners.

کلیدواژه‌ها English

Assessment
Personalization
Semantic Web
Personalized Assessment
طاهری، بهناز، طاهری، بهاره، رضوانی، دریا، سواری، علی، و سرداری، لیلا. (1402). بررسی نقش عوامل انگیزشی در یادگیری شخصی‌سازی‌شده و تأثیر آن بر مشارکت فراگیران، مجموعه مقالات چهارمین کنفرانس بین‏المللی مطالعات نوین در علوم انسانی، علوم تربیتی، حقوق و مطالعات اجتماعی، کپنهاک-دانمارک. https://civilica.com/doc/1962762
غریبه نیازی، منیره و رضایی شریف‏آبادی، سعید. (1395). شخصی‏سازی وب معنایی، مجموعه مقالات اولین کنفرانس بین‌المللی بازیابی تعاملی اطلاعات. https://civilica.com/doc/572888
مرادی، خدیجه. (1395). «فضای مفهومی» رویکردی جهت توسعه وب معنایی. بازیابی دانش و نظام‌های معنایی، 3(9)، 97 - 109. https://doi.org/10.22054/jks.2017.20825.1126
Abou El-Seoud, M. S., Karkar, A., Taj-Eddin, I. A., El-Sofany, H. F., Dandashi, A., & Al-Ja'am, J. M. (2015, September). Semantic-Web automated course management and evaluation system using mobile applications. In 2015 International Conference on Interactive Collaborative Learning (ICL) (pp. 271-282). IEEE. http://dx.doi.org/10.3991/ijim.v10i3.5770
Baneres, D., Baró, X., Guerrero-Roldán, A. E., & Rodríguez-González, M. E. (2016). Adaptive e-assessment system: A general approach. International Journal of Emerging Technologies in Learning, 2016, 11 (7). http://dx.doi.org/10.3991/ijet.v11i07.5888
Belcadhi, L. C. (2016). Personalized feedback for self assessment in lifelong learning environments based on semantic web. Computers in Human Behavior55, 562-570. https://doi.org/10.1016/j.chb.2015.07.042
Black, P., & Wiliam, D. (1998). Inside the Black Box: Raising Standards through Classroom Assessment. Phi Delta Kappan, 80(2), 139-148, https://doi.org/10.1177/003172171009200119
Chebbi, I. (2021). Ontological Model For Personalized the inclusive Learning. DOI: 10.13140/RG.2.2.21218.04800/1
Cheniti-Belcadhi, L., El Khayat, G., & Said. B. (2019, June 3-5). Knowledge Engineering for Competence Assessment on Serious Games Based on Semantic Web. 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). DOI: 10.1109/AIKE.2019.00037
Chitra, K., & Umamaheswari, R. (2018). Semantically Enchanced Personalised Adaptive E-Learning for General and Dyslexia Learners: An Ontology Based Approach. International Journal of Advanced Networking and Applications10(1), 3717-3723. https://www.proquest.com/scholarly-journals/semantically-enchanced-personalised-adaptive-e/docview/2099844184/se-2
Dhulekar, K., & Devrankar, M. (2020). A REVIEW ON SEMANTIC WEB. International Journal of Engineering Technologies and Management Research. DOI: https://doi.org/10.29121/ijetmr.v6.i12.2019.470
Filatov, V., Zolotukhin, A. (2019, September 6-8). Personalized Adaptation of Learning Environments. 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL). https://ieeexplore.ieee.org/abstract/document/9019525
Fradi, B., Cheniti-Belcadhi, L. (2022). Ontology Model For Smart Open Learning Environment Based On Coputational Thinking. International Conferences on Applied Computing 2022 and WWW/Internet 2022. ISBN: 978-989-8704-44-3. https://www.computing-conf.org/wp-content/uploads/2022/11/2_AC2022_F_040.pdf
Gavriushenko, M. (2017). On personalized adaptation of learning environments. 32-36. http://urn.fi/URN:ISBN:978-951-39-7287-5
Gharibe Niazi, M., Rezaei Sharifabadi, S. (2016). Semantic web personalization, The first international conference on interactive information retrieval. [In Persian]. https://civilica.com/doc/572888
Ghribi, R., Cheniti-Belcadhi, L. (2015, December 21-23). Towards feedback personalization in mobile assessment based on semantic web. 2015 5th International Conference on Information & Communication Technology and Accessibility (ICTA). DOI: 10.1109/ICTA.2015.7426919
Gosain, K. (2024). A Study on Understanding the Mediation of Web 3.0 Technologies in The Teaching-Learning Process and its effect on the Student's Achievement. International Research Journal on Advanced Engineering and Management (IRJAEM)2(04), 948-956. https://doi.org/10.47392/IRJAEM.2024.0126
Hadyaoui, A., Cheniti-Belcadhi, L. (2022, December 5-8). Towards a context-aware personalized formative assessment in a collaborative online environment. 19th International Conference on Computer Systems and Applications (AICCSA). https://doi.org/10.1109/AICCSA56895.2022.10017682
Hadyaoui, A., Cheniti-Belcadhi, L. (2023, October 3-5). Intelligent Collaborative Assessment for Cyberspace eLearning Environments. 2023 International Conference on Cyberworlds (CW). https://doi.org/10.1109/CW58918.2023.00054
Hajjej, F., Hlaoui, Y. B., & Ben Ayed, L. J. (2017, July 4-8). Cloud Adapted Workflow e-Assessment System: Cloud-AWAS. 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC). DOI: 10.1109/COMPSAC.2017.86
Harchay, A., Cheniti-Belcadhi, L., & Braham, R. (2015). A Context-aware Approach for Personalized Mobile Self-Assessment. Journal of Universal Computer Science, 21(8), 1061-1085. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=f1ff126 d7d6c6dff3f3754cd857b2d93e82b4002
Harchay, A., Cheniti-Belcadhi, L., & Braham, R. (2017, October 30- November 3). MobiSWAP: Personalized Mobile Assessment Tool Based on Semantic Web and Web Services. 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) DOI: 10.1109/AICCSA.2017.143
Ivanova, T. (2019, June 21-22). Resources and Semantic-based knowledge models for personalized and self-regulated learning in the Web: survey and trends. CompSysTech '19: Proceedings of the 20th International Conference on Computer Systems and Technologies. 316-323. https://doi.org/10.1145/3345252.3345288
Ivanova, T. (2023, September 20-21). Semantics-Based Knowledge Representation and Personalized Learning Content Development. 2023 International Conference on Information Technologies. (InfoTech). https://doi.org/10.1109/InfoTech58664.2023.10266887
Jayasiriwardene, Sh., & Meedeniya, D. (2022, February 23-24). A Knowledge-based Adaptive Algorithm to Recommend Interactive Learning Assessments. 2022 2nd International Conference on Advanced Research in Computing (ICARC). https://ieeexplore.ieee.org/abstract/document/9753913
Justo-López, A., López-Morteo, G., Flores-Ríos, B., & García, L. C. (2021). Process pattern and process capability evaluation model for interoperability in learning object environments. Array10, 100059. https://doi.org/10.1016/j.array.2021.100059
Kiselev, B., Yakutenko, V. (2020). An Overview of Massive Open Online Course Platforms: Personalization and Semantic Web Technologies and Standards. Procedia Computer Science, 169 (2020), 373–379. https://doi.org/10.1016/j.procs.2020.02.232
Moradi, Kh. (2016). An approach to the development of the semantic web "conceptual space". Scientific studies. Third year, number 9. 97-109. [In Persian]. https://doi.org/10.22054/jks.2017.20825.1126
Popov, M., Ivanova, T. (2020, June 19-20). Knowledge Model for Developing, Searching and Using Personalized Learning Content for Learners, Having Dyslexia Disability. CompSysTech '20: Proceedings of the 21st International Conference on Computer Systems and Technologies, 258 – 265. https://doi.org/10.1145/3407982.3407997
Sahin, M., Ifenthaler, D. (2024). Foundations of Assessment Analytics. In: Sahin, M., Ifenthaler, D. (eds) Assessment Analytics in Education. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-031-56365-2_1
Serhan, B., Said, B., Cheniti, L., & Khayat, G.EL. (2019, November 11-13).  Personalization in Serious Games for Assessment. 12th annual International Conference of Education, Research and Innovation. Seville, Spain. DOI: 10.21125/iceri.2019.1187
Signer, B., Ilkou, E. (2020, May 2-4). A Technology-enhanced Smart Learning Environment based on the Combination of Knowledge Graphs and Learning Paths. 12th International Conference on Computer Supported Education. DOI:10.5220/0009575104610468
Srisawasdi, N., Panjaburee, P. (2017). A Development of Supervised-Online Personal Learning Environment: Examining Factors affecting Self-directed Learning and Conceptual Understanding Progression. 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), 607-612. https://www.webofscience.com/wos/woscc/full-record/WOS:000454603400114
Srivastava, B., Tanwir Uddin Haider, Md. (2020). Personalized assessment model for alphabets learning with learning objects in e-learning environment for dyslexia. Journal of King Saud University – Computer and Information Sciences 32 (2020) 809–817. https://doi.org/10.1016/j.jksuci.2017.11.005
Taheri, B., Taheri, B., Rezvani, D., Savari, A., & Sardari, L. (2023). Investigating the role of motivational factors in personalized learning and its effect on learners' participation. [In Persian]. https://civilica.com/doc/1962762
Thaker, K., Zhang, L., He, D., & Brusilovsky, P. (2020, July 10-13). Recommending Remedial Readings Using Student Knowledge State. Proceedings of the 13th International Conference on Educational Data Mining (EDM 2020). https://files.eric.ed.gov/fulltext/ED608063.pdf
Wong, J., Baars, M., de Koning, B., van der Zee, T., Davis, D., Khalil, M., Geert-Jan, H., & Paas, F. (2019). Educational Theories and Learning Analytics: From Data to Knowledge. https://link.springer.com/chapter/10.1007/978-3-319-64792-0_1
Yuyun, I., & Suherdi, D. (2023, May). Components and Strategies for Personalized Learning in Higher Education: A Systematic Review. In 20th AsiaTEFL-68th TEFLIN-5th iNELTAL Conference (ASIATEFL 2022) (pp. 271-290). Atlantis Press. https://doi.org/10.2991/978-2-38476-054-1_23

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