臺灣人文及社會科學引文索引資料庫系統

:::

詳目顯示

回上一頁
第 1 筆 / 總合 1 筆   第一頁 上一頁 下一頁 最後一頁
/1頁
題名：情感分析於早期識別學習困難之研究
作者：李振瑋
作者(外文)：Cheng-Wei Lee
校院名稱：元智大學
系所名稱：資訊管理學系
指導教授：禹良治
學位類別：博士
出版日期：2018
主題關鍵詞：情感分析；學習分析；早期識別；非結構化資料；Sentiment analysis；learning analytics；early prediction；unstructured data
原始連結：連回原系統網址new window
相關次數：
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:4
本論文提出預測模型，期望於學期初期階段識別出遭遇學習困難的學生，試圖改善學習效率，減低學生於期末發生不合格的情況。為了增進早期識別的準確率，論文中採用情感分析（Sentiment analysis）技術對於同學們課後寫下的學習心得（Self-evaluated comment）進行分析，萃取出每位同學於學習過程中的情感狀態。實驗結果顯示，加入同學們的學習情緒，對於模型預測的準確率有顯著的提升效果，能更有效的於早期階段識別出學習困難的學生。
本論文改善了傳統上早期預測僅使用結構化資料（Structured data）進行分析的能力限制，因為諸如：作業成績、課堂出席率、小考成績…等結構化資料，在學期初期階段，往往會遇到資料量不足的問題，導致模型的預測能力偏低。因此，使用情感分析技術來處理非結構化資料（Unstructured data），例如：由學習心得中萃取出同學的學習情緒，將有助於增加早期識別的準確率，更有效的在學期初期階段，識別出遭遇學習困難的學生。
有鑑於學習情緒與學習的動機及成效具有顯著的關連性，本論文將學習心得萃取出的情緒資訊，應用於學習成效診斷暨自主學習系統（Dynamic Diagnostic and Self-regulated system），擁有正向學習情緒的學生可藉由系統中及時的學習診斷資訊與學習建議，發展成為自主學習型的學生（Self-regulated learner）；對於學習產生負向情緒的學生，授課者可以採用外部輔導（External guidance）的方式幫助學生改善學習成效，提升學習的有效性。
This study presents a model for the early identification of students likely to fail in an academic course. To enhance predictive accuracy, sentiment analysis is used to identify affective information from text-based self-evaluated comments written by students. Experimental results demonstrated that adding extracted sentiment information from student self-evaluations yields a significant improvement in early stage prediction quality. The results also indicate the limited early stage predictive value of structured data, such as homework completion, attendance, exam grades, etc., due to data sparseness at the beginning of the course. Thus, applying sentiment analysis to unstructured data (e.g., self-evaluation comments) can play an important role in improving the accuracy of early-stage predictions. The findings present educators with an opportunity to provide students with real-time feedback and support to help students become self‐regulated learners. Using the exploring results for improvement in teaching and learning initiatives is important to maintain students’ performances and the effectiveness of learning process.
[1] Altrabsheh, N. (2015). Predicting Learning-Related Emotions from Students' Textual Classroom Feedback via Twitter. Paper presented at the 8th International Conference on Educational Data Mining, Madrid, Spain, Jun 26-29.
[2] Arnold, K. E. (2010). Signals: Applying academic analytics. Educause Quarterly, 33(1), n1.
[3] Asghar, M. Z., Khan, A., Ahmad, S., Khan, I. A., & Kundi, F. M. (2015). A unified framework for creating domain dependent polarity lexicons from user generated reviews. PloS one, 10(10), e0140204.
[4] Baccianella, S., Esuli, A., & Sebastiani, F. (2010). Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. Paper presented at the LREC.
[5] Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics Learning analytics (pp. 61-75): Springer.
[6] Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. JEDM-Journal of Educational Data Mining, 1(1), 3-17.
[7] Banea, C., Mihalcea, R., & Wiebe, J. (2013). Porting multilingual subjectivity resources across languages. IEEE Transactions on Affective Computing, 4(2), 211-225.
[8] Basak, D., Pal, S., & Patranabis, D. C. (2007). Support vector regression. Neural Information Processing-Letters and Reviews, 11(10), 203-224.
[9] Bayer, J., Bydzovská, H., Géryk, J., Obsivac, T., & Popelinsky, L. (2012). Predicting Drop-Out from Social Behaviour of Students. International Educational Data Mining Society.
[10] Bradley, M. M., & Lang, P. J. (1999). Affective norms for English words (ANEW): Instruction manual and affective ratings. Retrieved from
[11] Bradley, M. M., & Lang, P. J. (2007). Affective Norms for English Text (ANET): Affective ratings of text and instruction manual. Techical Report. D-1, University of Florida, Gainesville, FL.
[12] Brooks, J. H., & Dubois, D. L. (1995). Individual and environmental predictors of adjustment during the first year of college. Journal of college student development.
[13] Bydžovská, H. (2016). A comparative analysis of techniques for predicting student performance. Paper presented at the Proceedings of the 9th International Conference on Educational Data Mining 2016.
[14] Calvo, R. A., & Mac Kim, S. (2013). Emotions in text: dimensional and categorical models. Computational Intelligence, 29(3), 527-543.
[15] Campbell, J. P. (2007). Utilizing student data within the course management system to determine undergraduate student academic success: An exploratory study: Purdue University.
[16] Chen, G.-D., Liu, C.-C., Ou, K.-L., & Liu, B.-J. (2000). Discovering decision knowledge from web log portfolio for managing classroom processes by applying decision tree and data cube technology. Journal of Educational Computing Research, 23(3), 305-332.
[17] Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37-46.
[18] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
[19] Cortez, P., & Silva, A. M. G. (2008). Using data mining to predict secondary school student performance.
[20] D’Mello, S., Jackson, T., Craig, S., Morgan, B., Chipman, P., White, H., . . . Picard, R. (2008). AutoTutor detects and responds to learners affective and cognitive states. Paper presented at the Workshop on emotional and cognitive issues at the international conference on intelligent tutoring systems.
[21] Dekker, G., Pechenizkiy, M., & Vleeshouwers, J. (2009). Predicting students drop out: A case study. Paper presented at the Educational Data Mining 2009.
[22] Drucker, H., Burges, C. J., Kaufman, L., Smola, A., & Vapnik, V. (1997). Support vector regression machines. Advances in neural information processing systems, 9, 155-161.
[23] Ekman, P. (1992). An argument for basic emotions. Cognition & emotion, 6(3-4), 169-200.
[24] Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89.
[25] Gökçay, D., İşbilir, E., & Yildirim, G. (2012). Predicting the sentiment in sentences based on words: An Exploratory Study on ANEW and ANET. Paper presented at the Cognitive Infocommunications (CogInfoCom), 2012 IEEE 3rd International Conference on.
[26] Gunes, H., & Schuller, B. (2013). Categorical and dimensional affect analysis in continuous input: Current trends and future directions. Image and Vision Computing, 31(2), 120-136.
[27] He, J., Bailey, J., Rubinstein, B. I., & Zhang, R. (2015). Identifying At-Risk Students in Massive Open Online Courses. Paper presented at the AAAI.
[28] Heimerl, F., Lohmann, S., Lange, S., & Ertl, T. (2014). Word cloud explorer: Text analytics based on word clouds. Paper presented at the System Sciences (HICSS), 2014 47th Hawaii International Conference on.
[29] Huang, C.-L., Chung, C. K., Hui, N., Lin, Y.-C., Seih, Y.-T., Lam, B. C., . . . Pennebaker, J. W. (2012). The development of the Chinese linguistic inquiry and word count dictionary. Chinese Journal of Psychology.
[30] Ifenthaler, D., & Widanapathirana, C. (2014). Development and validation of a learning analytics framework: Two case studies using support vector machines. Technology, Knowledge and Learning, 19(1-2), 221-240.
[31] Jayaprakash, S. M., Moody, E. W., Lauría, E. J., Regan, J. R., & Baron, J. D. (2014). Early alert of academically at-risk students: An open source analytics initiative. Journal of Learning Analytics, 1(1), 6-47.
[32] Kabakchieva, D. (2013). Predicting student performance by using data mining methods for classification. Cybernetics and Information Technologies, 13(1), 61-72.
[33] Kaur, P., Singh, M., & Josan, G. S. (2015). Classification and Prediction Based Data Mining Algorithms to Predict Slow Learners in Education Sector. Procedia Computer Science, 57, 500-508. doi:http://dx.doi.org/10.1016/j.procs.2015.07.372
[34] Kim, E., Newton, F. B., Downey, R. G., & Benton, S. L. (2010). Personal factors impacting college student success: Constructing college learning effectiveness inventory (CLEI). College Student Journal, 44(1), 112-126.
[35] Kiritchenko, S., & Mohammad, S. M. (2016). Capturing reliable fine-grained sentiment associations by crowdsourcing and best–worst scaling. Paper presented at the Proceedings of NAACL-HLT.
[36] Kiritchenko, S., Mohammad, S. M., & Salameh, M. (2016). SemEval-2016 Task 7: Determining sentiment intensity of English and Arabic phrases. Proceedings of SemEval, 42-51.
[37] Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Computers & Education, 104, 18-33.
[38] Ku, L. W., & Chen, H. H. (2007). Mining opinions from the Web: Beyond relevance retrieval. Journal of the Association for Information Science and Technology, 58(12), 1838-1850.
[39] Lang, P. J. (1980). Behavioral treatment and bio-behavioral assessment: Computer applications.
[40] Li, J., Ott, M., Cardie, C., & Hovy, E. H. (2014). Towards a General Rule for Identifying Deceptive Opinion Spam. Paper presented at the ACL (1).
[41] Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1-167.
[42] Luo, J., Sorour, S. E., Goda, K., & Mine, T. (2015). Predicting Student Grade Based on Free-Style Comments Using Word2Vec and ANN by Considering Prediction Results Obtained in Consecutive Lessons. International Educational Data Mining Society.
[43] Malandrakis, N., Potamianos, A., Iosif, E., & Narayanan, S. (2013). Distributional semantic models for affective text analysis. IEEE Transactions on Audio, Speech, and Language Processing, 21(11), 2379-2392.
[44] Mazza, R. (2009). Introduction to information visualization: Springer Science & Business Media.
[45] Minaei-Bidgoli, B., & Punch, W. F. (2003, 12-16 July). Using genetic algorithms for data mining optimization in an educational web-based system. Paper presented at the Genetic and evolutionary computation conference, Chicago, Illinois, USA.
[46] Mishne, G., & De Rijke, M. (2006). Capturing Global Mood Levels using Blog Posts. Paper presented at the AAAI spring symposium: computational approaches to analyzing weblogs.
[47] Nguyen, T., Phung, D., Dao, B., Venkatesh, S., & Berk, M. (2014). Affective and content analysis of online depression communities. IEEE Transactions on Affective Computing, 5(3), 217-226.
[48] Paltoglou, G., Theunis, M., Kappas, A., & Thelwall, M. (2013). Predicting emotional responses to long informal text. IEEE Transactions on Affective Computing, 4(1), 106-115.
[49] Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135.
[50] Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up?: sentiment classification using machine learning techniques. Paper presented at the Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10.
[51] Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students' self-regulated learning and achievement: A program of qualitative and quantitative research. Educational psychologist, 37(2), 91-105.
[52] Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications, 41(4, Part 1), 1432-1462. doi:http://dx.doi.org/10.1016/j.eswa.2013.08.042
[53] Pennebaker, J. W., Booth, R. J., & Francis, M. E. (2007). Linguistic inquiry and word count: LIWC [Computer software]. Austin, TX: liwc. net.
[54] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., & Manandhar, S. (2014). Semeval-2014 task 4: Aspect based sentiment analysis. Proceedings of SemEval, 27-35.
[55] Pritchard, M. E., & Wilson, G. S. (2003). Using emotional and social factors to predict student success. Journal of college student development, 44(1), 18-28.
[56] Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135-146. doi:http://dx.doi.org/10.1016/j.eswa.2006.04.005
[57] 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), 40(6), 601-618.
[58] Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384.
[59] Rosenthal, S., Nakov, P., Kiritchenko, S., Mohammad, S. M., Ritter, A., & Stoyanov, V. (2015). Semeval-2015 task 10: Sentiment analysis in twitter. Paper presented at the Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015).
[60] Russell, J. A. (1980). A circumplex model of affect. Journal of personality and social psychology, 39(6), 1161.
[61] Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for twitter sentiment analysis: a survey and a new dataset, the sts-gold.
[62] Santana, M. A., Costa, E. B., Fonseca, B., Rego, J., & de Araújo, F. F. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses. Computers in Human Behavior. doi:http://dx.doi.org/10.1016/j.chb.2017.01.047
[63] Santana, M. A., Costa, E. B., Neto, B. F. d. S., Silva, I. C. L., & Rego, J. B. (2015). A predictive model for identifying students with dropout profiles in online courses. Paper presented at the EDM (Workshops).
[64] Schouten, K., & Frasincar, F. (2016). Survey on aspect-level sentiment analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830.
[65] Shahiri, A. M., & Husain, W. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414-422.
[66] Shahiri, A. M., Husain, W., & Rashid, N. a. A. (2015). A Review on Predicting Student's Performance Using Data Mining Techniques. Procedia Computer Science, 72, 414-422. doi:http://dx.doi.org/10.1016/j.procs.2015.12.157
[67] Siemens, G. (2012). Learning analytics: envisioning a research discipline and a domain of practice. Paper presented at the Proceedings of the 2nd international conference on learning analytics and knowledge.
[68] Siemens, G., & d Baker, R. S. (2012). Learning analytics and educational data mining: towards communication and collaboration. Paper presented at the Proceedings of the 2nd international conference on learning analytics and knowledge.
[69] Simon, H. A. (1996). The Sciences of the Artificial, 3rd edn, orig. publ., 1969: Cambridge, MA: MIT Press.
[70] Smith, V. C., Lange, A., & Huston, D. R. (2012). Predictive modeling to forecast student outcomes and drive effective interventions in online community college courses. Journal of Asynchronous Learning Networks, 16(3), 51-61.
[71] Sorour, S. E., Goda, K., & Mine, T. (2015a). Evaluation of effectiveness of time-series comments by using machine learning techniques. Journal of Information Processing, 23(6), 784-794.
[72] Sorour, S. E., Goda, K., & Mine, T. (2017). Comment Data Mining to Estimate Student Performance Considering Consecutive Lessons. Journal of Educational Technology & Society, 20(1).
[73] Sorour, S. E., Mine, T., Goda, K., & Hirokawa, S. (2015b). A predictive model to evaluate student performance. Journal of Information Processing, 23(2), 192-201.
[74] Spence, R. (2001). Information visualization (Vol. 1): Springer.
[75] Stevenson, M. P., Hartmeyer, R., & Bentsen, P. (2017). Systematically reviewing the potential of concept mapping technologies to promote self-regulated learning in primary and secondary science education. Educational Research Review, 21, 1-16.
[76] Suthers, D., & Rosen, D. (2011). A unified framework for multi-level analysis of distributed learning. Paper presented at the Proceedings of the 1st international conference on learning analytics and knowledge.
[77] Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational linguistics, 37(2), 267-307.
[78] Thelwall, M., Buckley, K., & Paltoglou, G. (2012). Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63(1), 163-173.
[79] Tufte, E. R. (1983). The Visual Display of Quantitative Information. Cheshire, CT, USA: Graphics Press.
[80] Van Heyningen, J. (1998). Academic achievement in college students: What factors predict success?
[81] Wang, J. (2016). Sentiment analysis in continuous valence-arousal space. (Doctor of Philosophy), Yuan Ze University.
[82] Wang, J., Yu, L.-C., Lai, K. R., & Zhang, X. (2016). Community-based weighted graph model for valence-arousal prediction of affective words. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 24(11), 1957-1968.
[83] Wei, W.-L., Wu, C.-H., & Lin, J.-C. (2011). A regression approach to affective rating of Chinese words from ANEW Affective Computing and Intelligent Interaction (pp. 121-131): Springer.
[84] Wen, M., Yang, D., & Rose, C. (2014). Sentiment Analysis in MOOC Discussion Forums: What does it tell us? Paper presented at the Educational data mining 2014.
[85] Wiebe, J., Wilson, T., & Cardie, C. (2005). Annotating expressions of opinions and emotions in language. Language resources and evaluation, 39(2-3), 165-210.
[86] Xu, R., Gui, L., Xu, J., Lu, Q., & Wong, K.-F. (2015). Cross lingual opinion holder extraction based on multi-kernel SVMs and transfer learning. World wide web, 18(2), 299-316.
[87] You, J. W., & Kang, M. (2014). The role of academic emotions in the relationship between perceived academic control and self-regulated learning in online learning. Computers & Education, 77, 125-133.
[88] Yu, L., Lee, C., Pan, H., Chou, C., Chao, P., Chen, Z., . . . Lai, K. (2018). Improving early prediction of academic failure using sentiment analysis on self‐evaluated comments. Journal of Computer Assisted Learning.
[89] Yu, L.-C., Lee, L.-H., Hao, S., Wang, J., He, Y., Hu, J., . . . Zhang, X. (2016). Building Chinese affective resources in valence-arousal dimensions. Paper presented at the Proceedings of NAACL-HLT.
[90] Yu, L.-C., Liang, S.-F., Lin, W.-H., Lai, K. R., & Liu, B.-J. (2011). Analysis of Students’ Emotion from a Text Corpus. Paper presented at the Work-In-Progress Proceedings of the 19th International Conference on Computers in Education (ICCE-11.
[91] Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational psychologist, 25(1), 3-17.
[92] Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into practice, 41(2), 64-70.
[93] Zimmerman, B. J., & Martinez-Pons, M. (1988). Construct validation of a strategy model of student self-regulated learning. Journal of educational psychology, 80(3), 284.
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE

臺灣人文及社會科學引文索引資料庫

目前上線人數：569（臺灣：562，外國：7） ／ 本年度總使用人次：2111296 ／ 訪客人次（自102年09月）：67916324
國家圖書館著作權聲明 Copyright © 2013 All rights reserved.
本館地址: 100201臺北市中山南路20號． 本館地圖及交通資訊．
總機：(02)23619132．最佳瀏覽解析度為1024x768以上