ÖĞRENME ANALİTİĞİ DÜZEYLERİ BAĞLAMINDA ÖĞRENME PANELİ TASARIMINA YÖNELİK ÖĞRENEN BEKLENTİLERİNİN BELİRLENMESİ

Yıl: 2023 Cilt: 13 Sayı: 1 Sayfa Aralığı: 161 - 184 Metin Dili: Türkçe DOI: 10.17943/etku.1124933 İndeks Tarihi: 18-07-2023

ÖĞRENME ANALİTİĞİ DÜZEYLERİ BAĞLAMINDA ÖĞRENME PANELİ TASARIMINA YÖNELİK ÖĞRENEN BEKLENTİLERİNİN BELİRLENMESİ

Öz:
Bu çalışma, öğrenenlerin öğrenme analitiği düzeyleri bağlamında öğrenme panelinde yer almasını bekledikleri öğeleri keşfetmeyi ve buna uygun tasarım ilkeleri ortaya koymayı amaçlayan bir durum çalışmasıdır. Bu kapsamda daha önce e-öğrenme deneyimi olan 20 lisansüstü öğrencisiyle odak grup görüşmeleri gerçekleştirilmiştir. Odak grup görüşmeleri 5 farklı oturumda gerçekleştirilmiş ve her oturum ortalama 53 dakika sürmüştür. Görüşmelerden elde edilen veriler içerik analizi yöntemiyle çözümlenmiştir. Araştırma sonucunda elde edilen bulgular; dördü öğrenme analitiği düzeyleri (betimleyici analitikler, tanılayıcı analitikler, yordayıcı analitikler, öngörü analitikleri) kapsamında öğrenme panelinde yer alması gereken bilgilere yönelik beklentiler, biri ise bu bilgilerin öğrenme panelinde ne şekilde organize edilip sunulacağına ilişkin beklentiler olmak üzere beş alt başlık altında analiz edilip yorumlanmıştır. Katılımcılar betimleyici analitikler kapsamında öğrenme hedeflerine göre ne durumda olduklarına, gruba/sınıfa göre performanslarının nasıl olduğuna ilişkin bilgiler görmek istediklerini belirtmişlerdir. Tanılayıcı analitikler kapsamında ise katılımcılar öğrenme eksikliklerinin tespiti, performanslarındaki değişimlerin saptanması ve performans ile harcanan zaman ilişkisinin gösterimi ile ilgili bilgileri görmek istediklerini ifade etmişlerdir. Yordayıcı analitikler kapsamında başarı kestirimlerinin sunulması yaygın olarak beklenirken öngörü analitikleri kapsamında buna ek olarak başarılı olmak için nasıl bir yol izlemesi gerektiğine ilişkin bilgiler sunulması beklenmiştir. Çalışmada ayrıca öğrenme analitiği düzeylerinden bağımsız olarak öğrenenlerin öğrenme paneli tasarımına yönelik genel beklentileri sunulmuştur. Son olarak öğrenme analitiği düzeyleri bağlamında öğrenme panelinin tasarımına yönelik tasarım ilkeleri sunulmuştur.
Anahtar Kelime: öğrenme paneli betimleyici analitikler tanılayıcı analitikler yordayıcı analitikler öngörü analitikleri

IDENTIFYING LEARNERS’ EXPECTATIONS FROM LEARNING ANALYTICS DASHBOARDS IN THE CONTEXT OF ANALYTICS TYPES

Öz:
This case study aims to discover elements that learners expect from learning analytics dashboards and propose design principles based on these expectations. Focus group interviews were conducted with 20 graduate students with previous e-learning experience to inform design principles for learning analytics dashboards. Interviews were conducted with 5 groups, lasting an average of 53 minutes. The gathered information was analyzed through content analysis. The research findings were divided into five themes: four were expectations for information within the scope of learning analytics types (descriptive analytics, diagnostic analytics, predictive analytics, predictive analytics), and one theme was generic expectations for how the learning analytics dashboard should be designed. For descriptive analytics, respondents indicated that they would like to see information on how they were performing in relation to their learning objectives, as well as how their performance compared to the group/class average. For diagnostic analytics, respondents indicated that they would like to see information about learning deficiencies, performance anomalies, as well as the relation between time spent on a topic and performance. Although estimations of success within the context of predictive analytics are widely expected, information on how to follow a path to be successful within the context of prescriptive analytics is also expected. The study also presented the general expectations of learners from the learning analytics dashboards. Finally, design principles for learning analytics dashboards regarding learning analytics types are presented.
Anahtar Kelime: Learning analytics dashboards descriptive analytics diagnostic analytics predictive analytics prescriptive analytics

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
0
0
0
  • 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, 37, 13-49.
  • Alwafi, E. M. (2022). Designing an online discussion strategy with learning analytics feedback on the level of cognitive presence and student interaction in an online learning community. Online Learning, 26(1).
  • Bodily, R., & Verbert, K. (2017). Review of research on student-facing learning analytics dashboards and educational recommender systems. IEEE Transactions on Learning Technologies, 10(4), 405-418.
  • Brock, T. R. (2017). Performance analytics: The missing big data link between learning analytics and business analytics. Performance Improvement, 56(7), 6-16.
  • Brown, A., & Green, T. (2018). Issues and trends in instructional technology: Consistent growth in online learning, digital content, and the use of mobile technologies. In Educational media and technology yearbook (ss. 61-71). Springer, Cham.
  • Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE review, 42(4), 40.
  • Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318-331.
  • Conole, G., & Alevizou, P. (2010). A literature review of the use of Web 2.0 tools in Higher Education. A report commissioned by the Higher Education Academy.
  • Cuban, L. (1986). Teachers and machines: The classroom use of technology since 1920. Teachers College Press.
  • Delen, D., & Ram, S. (2018). Research challenges and opportunities in business analytics. Journal of Business Analytics, 1(1), 2-12.
  • Deshpande, P. S., Sharma, S. C., & Peddoju, S. K. (2019). Predictive and prescriptive analytics in big-data era. Security and data storage aspect in cloud computing (ss. 71-81). Springer, Singapore.
  • Du, X., Yang, J., Shelton, B. E., Hung, J. L., & Zhang, M. (2021). A systematic meta-review and analysis of learning analytics research. Behaviour & information technology, 40(1), 49- 62.
  • Ellis, C. (2013). Broadening the scope and increasing the usefulness of learning analytics: The case for assessment analytics. British Journal of Educational Technology, 44(4), 662-664.
  • Fong, S., Deb, S., & Yang, X. S. (2018). How meta-heuristic algorithms contribute to deep learning in the hype of big data analytics. In Progress in intelligent computing techniques: theory, practice, and applications (ss. 3-25). Springer, Singapore.
  • Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.
  • 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, 67(5), 1273-1306.
  • Herodotou, C., Rienties, B., Hlosta, M., Boroowa, A., Mangafa, C., & Zdrahal, Z. (2020). The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study. The Internet and Higher Education, 45, 100725.
  • Hilliger, I., Ortiz-Rojas, M., Pesántez-Cabrera, P., Scheihing, E., Tsai, Y. S., Muñoz-Merino, P. J., ... & Pérez-Sanagustín, M. (2020). Identifying needs for learning analytics adoption in Latin American universities: A mixed-methods approach. The Internet and Higher Education, 45, 100726.
  • Hindle, G., Kunc, M., Mortensen, M., Oztekin, A., & Vidgen, R. (2020). Business analytics: Defining the field and identifying a research agenda. European Journal of Operational Research, 281(3), 483-490.
  • Hsu, Y. C., Hung, J. L., & Ching, Y. H. (2013). Trends of educational technology research: More than a decade of international research in six SSCI-indexed refereed journals. Educational Technology Research and Development, 61(4), 685-705.
  • Howell, J. A., Roberts, L. D., & Mancini, V. O. (2018). Learning analytics messages: Impact of grade, sender, comparative information and message style on student affect and academic resilience. Computers in Human Behavior, 89, 8-15.
  • Ifenthaler, D. (2015). Learning analytics. In J. M. Spector (Ed.), The SAGE encyclopedia of educational technology (Vol. 2, ss. 447–451). Thousand Oaks: Sage.
  • Ifenthaler, D. (2017). Are higher education institutions prepared for learning analytics?. TechTrends, 61(4), 366-371.
  • Ifenthaler, D., Schumacher, C., & Sahin, M. (2021, July). System-based or Teacher-based Learning Analytics Feedback–What Works Best?. In 2021 International Conference on Advanced Learning Technologies (ICALT) (ss. 184-186). IEEE.
  • Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics to support study success in higher education: a systematic review. Educational Technology Research and Development, 68(4), 1961-1990.
  • Januszewski, A., & Molenda, M. (2008). Chapter 1: Definition. Educational technology: A definition with commentary. Lawrence Erlbaum Associates.
  • Jin, S. H. (2021). Educational Effects on the Transparency of Peer Participation Levels in Asynchronous Online Discussion Activities. IEEE Transactions on Learning Technologies, 14(5), 604-612.
  • Jivet, I., Scheffel, M., Schmitz, M., Robbers, S., Specht, M., & Drachsler, H. (2020). From students with love: An empirical study on learner goals, self-regulated learning and sense-making of learning analytics in higher education. The Internet and Higher Education, 47, 100758.
  • Jo, I. H., Yu, T., Lee, H., & Kim, Y. (2015). Relations between student online learning behavior and academic achievement in higher education: A learning analytics approach. In Emerging issues in smart learning (ss. 275-287). Springer, Berlin, Heidelberg.
  • Jones, K. M., Asher, A., Goben, A., Perry, M. R., Salo, D., Briney, K. A., & Robertshaw, M. B. (2020). “We're being tracked at all times”: Student perspectives of their privacy in relation to learning analytics in higher education. Journal of the Association for Information Science and Technology, 71(9), 1044-1059.
  • Joseph, L., Abraham, S., & Mani, B. P. (2022). Exploring the Effectiveness of Learning Path Recommendation based on Felder-Silverman Learning Style Model: A Learning Analytics Intervention Approach. Journal of Educational Computing Research, 07356331211057816.
  • Karaoglan Yilmaz, F. G., & Yilmaz, R. (2020). Student opinions about personalized recommendation and feedback based on learning analytics. Technology, knowledge and learning, 25(4), 753-768.
  • Karaoglan Yilmaz, F. G., & Yilmaz, R. (2021). Learning analytics as a metacognitive tool to influence learner transactional distance and motivation in online learning environments. Innovations in Education and Teaching International, 58(5), 575-585.
  • Karaoglan Yilmaz, F. G. (2022). Utilizing learning analytics to support students' academic self- efficacy and problem-solving skills. The Asia-Pacific Education Researcher, 31(2), 175- 191.
  • Karaoglan Yilmaz, F. G. (2022). The effect of learning analytics assisted recommendations and guidance feedback on students’ metacognitive awareness and academic achievements. Journal of Computing in Higher Education, 1-20.
  • Karaoglan Yilmaz, F. G., & Yilmaz, R. (2022). Learning analytics intervention improves students’ engagement in online learning. Technology, Knowledge and Learning, 27(2), 449-460.
  • Kew, S. N., & Tasir, Z. (2022). Developing a learning analytics intervention in e-learning to enhance students’ learning performance: A case study. Education and Information Technologies, 1-36.
  • Kimmons, R. (2020). Current trends (and missing links) in educational technology research and practice. TechTrends, 64(6), 803-809.
  • Kokoç, M., & Altun, A. (2021). Effects of learner interaction with learning dashboards on academic performance in an e-learning environment. Behaviour & Information Technology, 40(2), 161-175.
  • Lai, J. W., & Bower, M. (2020). Evaluation of technology use in education: Findings from a critical analysis of systematic literature reviews. Journal of Computer Assisted Learning, 36(3), 241-259.
  • Leung, A. C. M., Santhanam, R., Kwok, R. C. W., & Yue, W. T. (2022). Could Gamification Designs Enhance Online Learning Through Personalization? Lessons from a Field Experiment. Information Systems Research.
  • Li, M., Chen, Y., & Luo, H. (2020, August). Effects of Grouping Strategies on Asynchronous Online Discussion: Evidence From Learning Analytics and Social Network Analysis. In 2020 International Symposium on Educational Technology (ISET) (ss. 273-276). IEEE.
  • 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, 72, 101202.
  • Molenaar, I., Knoop-van Campen, C. A., & Hasselman, F. (2017, March). The effects of a learning analytics empowered technology on students' arithmetic skill development. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (ss. 614-615).
  • Nouira, A., Cheniti Belcadhi, L., & Braham, R. (2019). An ontology based framework of assessment analytics for massive learning. Computer Applications in Engineering Education, 27(6), 1343-1360.
  • Olson, T. M., & Wisher, R. A. (2002). The effectiveness of web-based instruction: An initial inquiry. International Review of Research in Open and Distributed Learning, 3(2), 1-17.
  • Pan, Z., & Liu, M. (2022, March). The effects of learning analytics hint system in supporting students problem-solving. In LAK22: 12th International Learning Analytics and Knowledge Conference (ss. 77-86).
  • Papamitsiou, Z., & Economides, A. A. (2019). Exploring autonomous learning capacity from a self regulated learning perspective using learning analytics. British Journal of Educational Technology, 50(6), 3138-3155.
  • Pardo, A., Jovanovic, J., Dawson, S., Gašević, D., & Mirriahi, N. (2019). Using learning analytics to scale the provision of personalised feedback. British Journal of Educational Technology, 50(1), 128-138.
  • Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A. E., & Bañeres, D. (2022). Applying the UTAUT model to explain the students' acceptance of an early warning system in Higher Education. Computers & Education, 182, 104468.
  • Rienties, B., Boroowa, A., Cross, S., Kubiak, C., Mayles, K., & Murphy, S. (2016). Analytics4Action Evaluation Framework: A Review of Evidence-Based Learning Analytics Interventions at the Open University UK. Journal of Interactive Media in Education, 2016(1).
  • Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355.
  • Schumacher, C., & Ifenthaler, D. (2018). Features students really expect from learning analytics. Computers in human behavior, 78, 397-407.
  • Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A. (2020). Linking learning behavior analytics and learning science concepts: Designing a learning analytics dashboard for feedback to support learning regulation. Computers in Human Behavior, 107, 105512.
  • Sergis, S., & Sampson, D. G. (2016). School analytics: A framework for supporting school complexity leadership. In Competencies in teaching, learning and educational leadership in the digital age (ss. 79-122). Springer, Cham.
  • Shum, S. B., & Ferguson, R. (2012). Social learning analytics. Journal of educational technology & society, 15(3), 3-26.
  • Siemens, G., & Gasevic, D. (2012). Guest editorial-learning and knowledge analytics. Journal of Educational Technology & Society, 15(3), 1-2.
  • Susnjak, T., Ramaswami, G. S., & Mathrani, A. (2022). Learning analytics dashboard: a tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education, 19(1), 1-23.
  • Şahin, M., & Yurdugül, H. (2020). Educational data mining and learning analytics: past, present and future. Bartın University Journal of Faculty of Education, 9(1), 121-131.
  • Şahin, M., & Yurdugül, H. (2022). Çevrimiçi Öğrenenlerin E-öğrenme Ortamı Etkileşimlerinin Öğrenen Kontrolüne Dayalı Olarak İncelenmesi. Pamukkale Üniversitesi Eğitim Fakültesi Dergisi, (54), 248-271.
  • Teasley, S. D. (2017). Student facing dashboards: One size fits all?. Technology, Knowledge and Learning, 22(3), 377-384.
  • Tepgeç, M., & Ifenthaler, D. (2022). Learning analytics-based interventions: A systematic review of experimental studies. In Proceedings of the International Conference on Cognition and Exploratory Learning in Digital Age, 327-330.
  • Tsai, Y. S., & Gasevic, D. (2017, March). Learning analytics in higher education---challenges and policies: a review of eight learning analytics policies. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (ss. 233-242).
  • Ustun, A. B., Zhang, K., Karaoğlan-Yilmaz, F. G., & Yilmaz, R. (2022). Learning analytics based feedback and recommendations in flipped classrooms: an experimental study in higher education. Journal of Research on Technology in Education, 1-17.
  • Valle, N., Antonenko, P., Valle, D., Dawson, K., Huggins-Manley, A. C., & Baiser, B. (2021). The influence of task-value scaffolding in a predictive learning analytics dashboard on learners' statistics anxiety, motivation, and performance. Computers & Education, 173, 104288.
  • Valle, N., Antonenko, P., Valle, D., Sommer, M., Huggins-Manley, A. C., Dawson, K., ... & Baiser, B. (2021). Predict or describe? How learning analytics dashboard design influences motivation and statistics anxiety in an online statistics course. Educational Technology Research and Development, 69(3), 1405-1431.
  • Viberg, O., Khalil, M., & Baars, M. (2020, Mart). Self-regulated learning and learning analytics in online learning environments: A review of empirical research. In Proceedings of the tenth international conference on learning analytics & knowledge (ss. 524-533).
  • Viberg, O., Engström, L., Saqr, M., & Hrastinski, S. (2022). Exploring students’ expectations of learning analytics: A person-centered approach. Education and Information Technologies, 1-21.
  • Yang, C. C., Chen, I. Y., Akçapınar, G., Flanagan, B., & Ogata, H. (2021). Using a summarized lecture material recommendation system to enhance students’ preclass preparation in a flipped classroom. Educational Technology & Society, 24(2), 1-13.
  • Yılmaz, R. (2020). Enhancing community of inquiry and reflective thinking skills of undergraduates through using learning analytics based process feedback. Journal of Computer Assisted Learning, 36(6), 909-921.
  • Yilmaz, R., Yurdugül, H., Yilmaz, F. G. K., Şahı̇n, M., Sulak, S., Aydin, F., ... & Ömer, O. R. A. L. (2022). Smart MOOC integrated with intelligent tutoring: A system architecture and framework model proposal. Computers and Education: Artificial Intelligence, 3, 100092.
  • Wang, D., & Han, H. (2021). Applying learning analytics dashboards based on process oriented feedback to improve students' learning effectiveness. Journal of Computer Assisted Learning, 37(2), 487-499.
  • West, D., Luzeckyj, A., Toohey, D., Vanderlelie, J., & Searle, B. (2020). Do academics and university administrators really know better? The ethics of positioning student perspectives in learning analytics. Australasian Journal of Educational Technology, 36(2), 60-70.
  • Whitelock Wainwright, A., Gašević, D., Tejeiro, R., Tsai, Y. S., & Bennett, K. (2019). The student expectations of learning analytics questionnaire. Journal of Computer Assisted Learning, 35(5), 633-666.
  • Williamson, K., & Kizilcec, R. (2022, March). A review of learning analytics dashboard research in higher education: Implications for justice, equity, diversity, and inclusion. In LAK22: 12th International Learning Analytics and Knowledge Conference (ss. 260-270).
  • Yunita, A., Santoso, H. B., & Hasibuan, Z. A. (2021, Haziran). Research review on big data usage for learning analytics and educational data mining: A way forward to develop an intelligent automation system. In Journal of Physics: Conference Series (Vol. 1898, No. 1, p. 012044). IOP Publishing.
  • Zhang, J. H., Zou, L. C., Miao, J. J., Zhang, Y. X., Hwang, G. J., & Zhu, Y. (2020). An individualized intervention approach to improving university students’ learning performance and interactive behaviors in a blended learning environment. Interactive Learning Environments, 28(2), 231-245.
  • Zheng, L., Zhong, L., & Niu, J. (2022). Effects of personalised feedback approach on knowledge building, emotions, co-regulated behavioural patterns and cognitive load in online collaborative learning. Assessment & Evaluation in Higher Education, 47(1), 109-125.
  • Zheng, L., Niu, J., & Zhong, L. (2022). Effects of a learning analytics based real time feedback approach on knowledge elaboration, knowledge convergence, interactive relationships and group performance in CSCL. British Journal of Educational Technology, 53(1), 130- 149.
  • Zhou, Z. X., Tam, V., Lui, K. S., Lam, E. Y., Hu, X., Yuen, A., & Law, N. (2020, July). A sophisticated platform for learning analytics with wearable devices. In 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT) (ss. 300-304).
APA TEPGEÇ M, Yurdugül H (2023). ÖĞRENME ANALİTİĞİ DÜZEYLERİ BAĞLAMINDA ÖĞRENME PANELİ TASARIMINA YÖNELİK ÖĞRENEN BEKLENTİLERİNİN BELİRLENMESİ. , 161 - 184. 10.17943/etku.1124933
Chicago TEPGEÇ Mustafa,Yurdugül Halil ÖĞRENME ANALİTİĞİ DÜZEYLERİ BAĞLAMINDA ÖĞRENME PANELİ TASARIMINA YÖNELİK ÖĞRENEN BEKLENTİLERİNİN BELİRLENMESİ. (2023): 161 - 184. 10.17943/etku.1124933
MLA TEPGEÇ Mustafa,Yurdugül Halil ÖĞRENME ANALİTİĞİ DÜZEYLERİ BAĞLAMINDA ÖĞRENME PANELİ TASARIMINA YÖNELİK ÖĞRENEN BEKLENTİLERİNİN BELİRLENMESİ. , 2023, ss.161 - 184. 10.17943/etku.1124933
AMA TEPGEÇ M,Yurdugül H ÖĞRENME ANALİTİĞİ DÜZEYLERİ BAĞLAMINDA ÖĞRENME PANELİ TASARIMINA YÖNELİK ÖĞRENEN BEKLENTİLERİNİN BELİRLENMESİ. . 2023; 161 - 184. 10.17943/etku.1124933
Vancouver TEPGEÇ M,Yurdugül H ÖĞRENME ANALİTİĞİ DÜZEYLERİ BAĞLAMINDA ÖĞRENME PANELİ TASARIMINA YÖNELİK ÖĞRENEN BEKLENTİLERİNİN BELİRLENMESİ. . 2023; 161 - 184. 10.17943/etku.1124933
IEEE TEPGEÇ M,Yurdugül H "ÖĞRENME ANALİTİĞİ DÜZEYLERİ BAĞLAMINDA ÖĞRENME PANELİ TASARIMINA YÖNELİK ÖĞRENEN BEKLENTİLERİNİN BELİRLENMESİ." , ss.161 - 184, 2023. 10.17943/etku.1124933
ISNAD TEPGEÇ, Mustafa - Yurdugül, Halil. "ÖĞRENME ANALİTİĞİ DÜZEYLERİ BAĞLAMINDA ÖĞRENME PANELİ TASARIMINA YÖNELİK ÖĞRENEN BEKLENTİLERİNİN BELİRLENMESİ". (2023), 161-184. https://doi.org/10.17943/etku.1124933
APA TEPGEÇ M, Yurdugül H (2023). ÖĞRENME ANALİTİĞİ DÜZEYLERİ BAĞLAMINDA ÖĞRENME PANELİ TASARIMINA YÖNELİK ÖĞRENEN BEKLENTİLERİNİN BELİRLENMESİ. Eğitim Teknolojisi Kuram ve Uygulama, 13(1), 161 - 184. 10.17943/etku.1124933
Chicago TEPGEÇ Mustafa,Yurdugül Halil ÖĞRENME ANALİTİĞİ DÜZEYLERİ BAĞLAMINDA ÖĞRENME PANELİ TASARIMINA YÖNELİK ÖĞRENEN BEKLENTİLERİNİN BELİRLENMESİ. Eğitim Teknolojisi Kuram ve Uygulama 13, no.1 (2023): 161 - 184. 10.17943/etku.1124933
MLA TEPGEÇ Mustafa,Yurdugül Halil ÖĞRENME ANALİTİĞİ DÜZEYLERİ BAĞLAMINDA ÖĞRENME PANELİ TASARIMINA YÖNELİK ÖĞRENEN BEKLENTİLERİNİN BELİRLENMESİ. Eğitim Teknolojisi Kuram ve Uygulama, vol.13, no.1, 2023, ss.161 - 184. 10.17943/etku.1124933
AMA TEPGEÇ M,Yurdugül H ÖĞRENME ANALİTİĞİ DÜZEYLERİ BAĞLAMINDA ÖĞRENME PANELİ TASARIMINA YÖNELİK ÖĞRENEN BEKLENTİLERİNİN BELİRLENMESİ. Eğitim Teknolojisi Kuram ve Uygulama. 2023; 13(1): 161 - 184. 10.17943/etku.1124933
Vancouver TEPGEÇ M,Yurdugül H ÖĞRENME ANALİTİĞİ DÜZEYLERİ BAĞLAMINDA ÖĞRENME PANELİ TASARIMINA YÖNELİK ÖĞRENEN BEKLENTİLERİNİN BELİRLENMESİ. Eğitim Teknolojisi Kuram ve Uygulama. 2023; 13(1): 161 - 184. 10.17943/etku.1124933
IEEE TEPGEÇ M,Yurdugül H "ÖĞRENME ANALİTİĞİ DÜZEYLERİ BAĞLAMINDA ÖĞRENME PANELİ TASARIMINA YÖNELİK ÖĞRENEN BEKLENTİLERİNİN BELİRLENMESİ." Eğitim Teknolojisi Kuram ve Uygulama, 13, ss.161 - 184, 2023. 10.17943/etku.1124933
ISNAD TEPGEÇ, Mustafa - Yurdugül, Halil. "ÖĞRENME ANALİTİĞİ DÜZEYLERİ BAĞLAMINDA ÖĞRENME PANELİ TASARIMINA YÖNELİK ÖĞRENEN BEKLENTİLERİNİN BELİRLENMESİ". Eğitim Teknolojisi Kuram ve Uygulama 13/1 (2023), 161-184. https://doi.org/10.17943/etku.1124933