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Çokluortam Öğrenme Materyalinde Duygu Salınımını Belirleme

Yıl 2024, , 32 - 64, 15.01.2024
https://doi.org/10.9779/pauefd.1178733

Öz

Çokluortam öğrenme materyallerinin (ÇÖM) duygusal tasarımı ile öğrenenlerde belirli bir duygu oluşturma (olumlu-olumsuz), öğrenenlerin motivasyonlarını düzenleme, bilişsel özelliklerini ve öğrenme çıktılarını etkileme gibi hedefler güdülmektedir. Bununla birlikte, ÇÖM’lerin duygusal niteliğini sağlamak için belirli yönergelerin geliştirilmesi önemlidir. Bu çalışmada, metinleri açısından olumlu ve olumsuz duygusal tasarıma sahip iki ÇÖM’ün duygu salınımını belirlemek için duygu haritası modeli (DHM) kullanılmıştır. DHM sözlük (lexicon) tabanlı bir metin duygu analizi (sentiment analysis) aracıdır. DHM sürecine göre; öncelikle olumlu ve olumsuz ÇÖM’ler cümle cümle ayrılmış ve her bir cümlenin duygu tonu hesaplanmıştır. Ardından olumlu ve olumsuz ÇÖM’ler için hesaplanan duygu ton değerleri Shewhart Kontrol Diyagramı üzerine yerleştirilerek metinlerin duygu salınımları görselleştirilmiştir. Duygu salınımı içerisinde; istikrarlı, belirgin, baskın, şiddetli duyguların yer aldığı bölgeleri belirlemek için analiz kuralları uygulanmış ve bu bölgeler diyagram üzerinde belirginleştirilmiştir. Sonuç olarak, olumlu ÇÖM’deki duygu salınımlarında olumsuz ÇÖM’e göre daha fazla istikrarlı bölgenin yer aldığı görülmüştür. Bu bağlamda, ÇÖM’lerde yer alan metinlerin DHM ile duygu analizinin yapılması, ÇÖM’lerin duygusal tasarımı ve öğrenme-öğretme süreçlerinde kullanımı tartışılmıştır.

Kaynakça

  • Akgül, E. S., Ertano, C., & Diri, B. (2016). Sentiment analysis with Twitter. Pamukkale University Journal of Engineering Science, 22(2), 106-110.
  • Bradley, M. M., & Lang, P. J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1), 49-59.
  • Brom, C., Hannemann, T., Starkova, T., Bromova, E., & Dechterenko, F. (2016). Anthropomorphic faces and funny graphics in an instructional animation may improve superficial rather than deep learning: A quasi-experimental study. In J. Novoyn, & A. Jancarik (Eds.), Proceedings of the 15th European Conference on e-Learning, ECEL 2016 (pp. 89-97). Prague, Czech Republic: Academic Conferences and Publishing International Limited.
  • Clark, R. C., & Mayer, R. E. (2016). e-Learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning (4th ed.). Hooken, New Jersey: John Wiley & Sons.
  • Cobos, R., Jurado, F., & Blazquez-Herranz, A. (2019). A content analysis system that supports sentiment analysis for subjectivity and polarity detection in online courses. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 14(4), 177-187.
  • Dehkharghani, R., Saygin, Y., Yanikoglu, B., & Oflazer, K. (2016). SentiTurkNet: A Turkish polarity lexicon for sentiment analysis. Language Resources and Evaluation, 50(3), 667-685.
  • Dong, C. (2007). Positive emotions and learning: What makes a difference in multimedia design? (Unpublished master’s thesis). New York University, New York, USA.
  • Flemming, D. Cress, U., Kimming, S., Brandt, M., & Kimmerle, J. (2018). Emotionalization in science communication: The Impact of narratives and visual representation on knowledge gain and risk perception. Frontiers in Communication 3(3). DOI: 10.3389/fcomm.2018.00003
  • Kühl, T., & Zander, S. (2017). An inverted personalization effect when learning with multimedia: The case of aversive content. Computers & Education, 108, 71-84.
  • Liu, B. (2020). Sentiment analysis: Mining opinions, sentiments, and emotions (2nd Ed.). Cambridge: Cambridge University Press.
  • Mayer, R. E. (2009). Multimedia learning (2nd Ed.). Cambridge: Cambridge University Press.
  • Montgomery, D. C. (2009). Introduction to statistical quality control. Hoboken, NJ: John Wiley & Sons.
  • Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309-326.
  • Özcil, A. (2014). Shewhart, Cusum ve Ewma kontol grafiklerinin bir üretim işletmesinde uygulanması. Unpublished master thesis, Pamukkale University, Denizli, Turkey.
  • Özgür, A. (2021). The effect of working memory capacity and emotional design on engagement with multimedia learning materials. Unpublished doctoral dissertation, Hacettepe University, Ankara, Turkey.
  • Plass, J. L., Heidig, S., Hayward, E. O., Homer, B. D., & Um, E. (2014). Emotional design in multimedia learning: Effects of shape and color on affect and learning. Learning and Instruction, 29, 128-140.
  • Plass, J. L. & Kaplan, U. (2016). Emotional design in digital media for learning. In S. Y. Tettegah, & M. Gartmeier (Eds.). (2016). Emotions, Technology, Design, and Learning (pp. 131-161). London: Academic Press.
  • Sağlam, F. (2019). Automated sentiment lexicon generation and sentiment analysis of news. Unpublished doctoral dissertation, Hacettepe University, Ankara, Turkey.
  • Sağlam, F., Genç, B., & Sever, H. (2019). Extending a sentiment lexicon with synonym-antonym datasets: SWNetTR++. Turkish Journal of Electrical Engineering and Computer Sciences, 27, 1806-1820.
  • Souza, N., & Perry, G. (2018). Identification of affective states in MOOCs: A systematic literature review. International Journal for Innovation Education and Research,6(12), 39-55.
  • Stark, L., Brünken, R., & Park, B. (2018). Emotional text design in multimedia learning: A mixed-methods study using eye tracking. Computers & Education, 120, 185-196.
  • Taylor, S. S., & Statler, M. (2014). Materials matters: Increasing emotional engagement in learning. Journal of Management Education, 38(4), 586-607. DOI: 10.1177/1052562913489976
  • Um, E. R., Plass, J. L., Hayward, E. O., & Homer, B. D. (2011). Emotional design in multimedia learning. Journal of Educational Psychology, 104(2), 485-498.
  • Western Electric (1959). Statistical quality control handbook. Western Electric Company.
  • Yoldaş, İ. N. (2021) Türkçe metinlerde duygu analizi: Sözlük tabanlı Yaklaşım ve İnsanların Tepkilerinin Karşılaştırılması. ESTUDAM Bilişim Dergisi, 2(1), 1-6.

Detecting Sentiment Fluctuations in Multimedia Learning Material

Yıl 2024, , 32 - 64, 15.01.2024
https://doi.org/10.9779/pauefd.1178733

Öz

With the emotional design of multimedia learning materials (MLM), goals such as creating a certain emotion in learners (positive-negative), regulating learners' motivation, influencing their cognitive characteristics, and learning outcomes are pursued. Thus, it is important to develop certain guidelines to ensure the affective quality of the MLMs. In this study, the Sentiment Map Model (SMM) was used to detect the sentiment fluctuations of two MLMs with positive and negative emotional designs in terms of their texts. SMM is a lexicon-based sentiment analysis tool. As the first step in the SMM process, the sentences of positive and negative MLMs were determined. Then, using SWNetTR++ lexicon, the sentiment tones of the sentences were calculated. The calculated sentiment tone values for positive and negative MLMs were placed on the Shewhart Control Diagram and sentiment fluctuations of the sentences were visualized. Four analysis rules (stable, significant, strong and violent) were applied to find consistency regions in the sentiment fluctuations and those regions were highlighted in the diagram. As a result, it was observed that there were more stable sentiment fluctuation regions in the positive MLM than in the negative MLM. In this context, the sentiment analysis of the texts in the MLMs with SMM, the emotional design of the MLMs and their use in learning-teaching processes were discussed.

Kaynakça

  • Akgül, E. S., Ertano, C., & Diri, B. (2016). Sentiment analysis with Twitter. Pamukkale University Journal of Engineering Science, 22(2), 106-110.
  • Bradley, M. M., & Lang, P. J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1), 49-59.
  • Brom, C., Hannemann, T., Starkova, T., Bromova, E., & Dechterenko, F. (2016). Anthropomorphic faces and funny graphics in an instructional animation may improve superficial rather than deep learning: A quasi-experimental study. In J. Novoyn, & A. Jancarik (Eds.), Proceedings of the 15th European Conference on e-Learning, ECEL 2016 (pp. 89-97). Prague, Czech Republic: Academic Conferences and Publishing International Limited.
  • Clark, R. C., & Mayer, R. E. (2016). e-Learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning (4th ed.). Hooken, New Jersey: John Wiley & Sons.
  • Cobos, R., Jurado, F., & Blazquez-Herranz, A. (2019). A content analysis system that supports sentiment analysis for subjectivity and polarity detection in online courses. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 14(4), 177-187.
  • Dehkharghani, R., Saygin, Y., Yanikoglu, B., & Oflazer, K. (2016). SentiTurkNet: A Turkish polarity lexicon for sentiment analysis. Language Resources and Evaluation, 50(3), 667-685.
  • Dong, C. (2007). Positive emotions and learning: What makes a difference in multimedia design? (Unpublished master’s thesis). New York University, New York, USA.
  • Flemming, D. Cress, U., Kimming, S., Brandt, M., & Kimmerle, J. (2018). Emotionalization in science communication: The Impact of narratives and visual representation on knowledge gain and risk perception. Frontiers in Communication 3(3). DOI: 10.3389/fcomm.2018.00003
  • Kühl, T., & Zander, S. (2017). An inverted personalization effect when learning with multimedia: The case of aversive content. Computers & Education, 108, 71-84.
  • Liu, B. (2020). Sentiment analysis: Mining opinions, sentiments, and emotions (2nd Ed.). Cambridge: Cambridge University Press.
  • Mayer, R. E. (2009). Multimedia learning (2nd Ed.). Cambridge: Cambridge University Press.
  • Montgomery, D. C. (2009). Introduction to statistical quality control. Hoboken, NJ: John Wiley & Sons.
  • Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309-326.
  • Özcil, A. (2014). Shewhart, Cusum ve Ewma kontol grafiklerinin bir üretim işletmesinde uygulanması. Unpublished master thesis, Pamukkale University, Denizli, Turkey.
  • Özgür, A. (2021). The effect of working memory capacity and emotional design on engagement with multimedia learning materials. Unpublished doctoral dissertation, Hacettepe University, Ankara, Turkey.
  • Plass, J. L., Heidig, S., Hayward, E. O., Homer, B. D., & Um, E. (2014). Emotional design in multimedia learning: Effects of shape and color on affect and learning. Learning and Instruction, 29, 128-140.
  • Plass, J. L. & Kaplan, U. (2016). Emotional design in digital media for learning. In S. Y. Tettegah, & M. Gartmeier (Eds.). (2016). Emotions, Technology, Design, and Learning (pp. 131-161). London: Academic Press.
  • Sağlam, F. (2019). Automated sentiment lexicon generation and sentiment analysis of news. Unpublished doctoral dissertation, Hacettepe University, Ankara, Turkey.
  • Sağlam, F., Genç, B., & Sever, H. (2019). Extending a sentiment lexicon with synonym-antonym datasets: SWNetTR++. Turkish Journal of Electrical Engineering and Computer Sciences, 27, 1806-1820.
  • Souza, N., & Perry, G. (2018). Identification of affective states in MOOCs: A systematic literature review. International Journal for Innovation Education and Research,6(12), 39-55.
  • Stark, L., Brünken, R., & Park, B. (2018). Emotional text design in multimedia learning: A mixed-methods study using eye tracking. Computers & Education, 120, 185-196.
  • Taylor, S. S., & Statler, M. (2014). Materials matters: Increasing emotional engagement in learning. Journal of Management Education, 38(4), 586-607. DOI: 10.1177/1052562913489976
  • Um, E. R., Plass, J. L., Hayward, E. O., & Homer, B. D. (2011). Emotional design in multimedia learning. Journal of Educational Psychology, 104(2), 485-498.
  • Western Electric (1959). Statistical quality control handbook. Western Electric Company.
  • Yoldaş, İ. N. (2021) Türkçe metinlerde duygu analizi: Sözlük tabanlı Yaklaşım ve İnsanların Tepkilerinin Karşılaştırılması. ESTUDAM Bilişim Dergisi, 2(1), 1-6.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Adem Özgür 0000-0003-2019-2014

Fatih Sağlam 0000-0002-6818-3865

Burkay Genç 0000-0001-5134-1487

Arif Altun 0000-0003-4060-6157

Erken Görünüm Tarihi 18 Temmuz 2023
Yayımlanma Tarihi 15 Ocak 2024
Gönderilme Tarihi 22 Eylül 2022
Kabul Tarihi 3 Temmuz 2023
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Özgür, A., Sağlam, F., Genç, B., Altun, A. (2024). Çokluortam Öğrenme Materyalinde Duygu Salınımını Belirleme. Pamukkale Üniversitesi Eğitim Fakültesi Dergisi(60), 32-64. https://doi.org/10.9779/pauefd.1178733