Chapter 4 Student Knowledge Estimation with Bayesian Network
4.6 Conclusions
In this work, we firstly developed a data-driven approach to assess the latent student knowledge by constructing Bayesian student models. Then we put forward a novel algorithm to identify the misconceptions for each quiz question, and thus we could provide individualized and remedial interventions for each student. In the end, we proposed a novel index to evaluate the student models as well as measuring the design of each question. According to the results, we identified common distractors found for the concept inventory data. As the model is capable of discovering individualized misconception, it can provide timely intervention after each test. Furthermore, the measurement index showed that 20 of the 27 questions exhibit predictive capability with the student model, while several inproper-designed questions were discussed.
Question 7: A 200 N-mm couple acting counter-clockwise keeps the member in equilibrium while it is subjected to other forces acting in the plane (shown schematically at the left). The four dots denote equally spaced points along the member. Assuming the other forces stay the same, what load(s) could replace the 200 N-mm couple and maintain equilibrium?
Figure 4.9: Question 7 related to concept C2
Figure 4.10: Histogram of blind guessing in addition to the actual performance of students score.
Bibliography
[1] Hamed Abdelhaq, Christian Sengstock, and Michael Gertz. “EvenTweet: On- line Localized Event Detection from Twitter”. In: Proc. VLDB Endow. 6.12 (Aug. 2013), pp. 1326–1329.issn: 2150-8097.doi:10.14778/2536274.2536307.
url:http://dx.doi.org/10.14778/2536274.2536307.
[2] James Allan. “Introduction to topic detection and tracking”. In: Topic detec-
tion and tracking. Kluwer Academic Publishers, 2002, pp. 1–16. isbn: 0-7923-
7664-1.
[3] Vicki L Almstrum et al. “Concept inventories in computer science for the topic discrete mathematics”. In: ACM SIGCSE Bulletin. Vol. 38(4). ACM. 2006, pp. 132–145.
[4] Dianne L Anderson, Kathleen M Fisher, and Gregory J Norman. “Develop- ment and evaluation of the conceptual inventory of natural selection”. In:
Journal of research in science teaching 39.10 (2002), pp. 952–978.
[5] Rebecca A Atadero et al. “Project-Based Learning in Statics: Curriculum, Student Outcomes, and On-going Questions”. In: age 24 (2014), p. 1.
[6] Farzindar Atefeh and Wael Khreich. “A Survey of Techniques for Event Detec- tion in Twitter”. In:Comput. Intell.31.1 (2015), pp. 132–164.issn: 0824-7935.
doi:10.1111/coin.12017.
[7] Janelle Margaret Bailey. Development of a Concept Inventory to Assess Stu- dents’ Understanding and Reasoning Difficulties About the Properties and For-
mation of Stars. 2006. url:http://hdl.handle.net/10150/193643.
[8] Nilesh Bansal and Nick Koudas. “BlogScope: Spatio-temporal Analysis of the Blogosphere”. In: Proceedings of the 16th International Conference on World
Wide Web. WWW ’07. New York, NY, USA: ACM, 2007, pp. 1269–1270.isbn:
978-1-59593-654-7. doi: 10.1145/1242572.1242802. url: http://doi.acm. org/10.1145/1242572.1242802.
[9] H. Becker, M. Naaman, and L. Gravano. “Beyond trending topics: Real-world event identification on Twitter”. In:Fifth International AAAI Conference on
Weblogs and Social Media. 2011.
[10] David M. Blei. “Probabilistic Topic Models”. In: Commun. ACM 55.4 (Apr. 2012), pp. 77–84. issn: 0001-0782. doi: 10 . 1145 / 2133806 . 2133826. url:
http://doi.acm.org/10.1145/2133806.2133826.
[11] Charles Blundell et al. “Weight Uncertainty in Neural Networks”. In: Pro- ceedings of the 32Nd International Conference on International Conference
on Machine Learning - Volume 37. ICML’15. Lille, France: JMLR.org, 2015,
pp. 1613–1622. url: http : / / dl . acm . org / citation . cfm ? id = 3045118 . 3045290.
[12] Stacey Lowery Bretz and Kimberly J Linenberger. “Development of the enzyme– substrate interactions concept inventory”. In:Biochemistry and Molecular Bi-
ology Education 40.4 (2012), pp. 229–233.
[13] Thang D. Bui et al. “Deep Gaussian Processes for Regression using Approx- imate Expectation Propagation”. In: ICML. Vol. 48. JMLR Workshop and Conference Proceedings. JMLR.org, 2016, pp. 1472–1481.
[14] Gregoire Burel et al. “On Semantics and Deep Learning for Event Detection in Crisis Situations”. In: ESWC 2017. Portoroz, Slovenia, 2017.
[15] C. J. Butz, S. Hua, and R. B. Maguire. “A Web-based Bayesian Intelligent Tutoring System for Computer Programming”. In: Web Intelli. and Agent Sys. 4.1 (Jan. 2006), pp. 77–97. issn: 1570-1263. url: http://dl.acm.org/ citation.cfm?id=1239784.1239789.
[16] SM Case and DB Swanson. Item writing manual: Constructing written test
questions for the basic and clinical sciences. 2002.
[17] Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. “Information Cred- ibility on Twitter”. In: Proceedings of the 20th International Conference on
World Wide Web. WWW ’11. Hyderabad, India: ACM, 2011, pp. 675–684.
isbn: 978-1-4503-0632-4. doi: 10 . 1145 / 1963405 . 1963500. url: http : / / doi.acm.org/10.1145/1963405.1963500.
[18] Mario Cataldi, Luigi Di Caro, and Claudio Schifanella. “Emerging Topic De- tection on Twitter Based on Temporal and Social Terms Evaluation”. In:Pro-
ceedings of the Tenth International Workshop on Multimedia Data Mining.
MDMKDD ’10. Washington, D.C.: ACM, 2010, 4:1–4:10. isbn: 978-1-4503- 0220-3. doi: 10.1145/1814245.1814249. url: http://doi.acm.org/10. 1145/1814245.1814249.
[19] Junghoon Chae et al. “Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition”. In:
2012 IEEE Conference on Visual Analytics Science and Technology, VAST
2012, Seattle, WA, USA, October 14-19, 2012. 2012, pp. 143–152. doi: 10.
1109/VAST.2012.6400557. url: https://doi.org/10.1109/VAST.2012. 6400557.
[20] Deepayan Chakrab. and Kunal Punera. “Event Summarization Using Tweets”. In: (2011).
[21] A. L. Chandrasegaran, David F. Treagust, and Mauro Mocerino. “The de- velopment of a two-tier multiple-choice diagnostic instrument for evaluating secondary school students’ ability to describe and explain chemical reactions using multiple levels of representation”. In: Chem. Educ. Res. Pract. 8 (3 2007), pp. 293–307.
[22] Ling Chen and Abhishek Roy. “Event Detection from Flickr Data Through Wavelet-based Spatial Analysis”. In:Proceedings of the 18th ACM Conference
on Information and Knowledge Management. CIKM ’09. Hong Kong, China:
ACM, 2009, pp. 523–532. isbn: 978-1-60558-512-3. doi: 10.1145/1645953. 1646021. url: http://doi.acm.org/10.1145/1645953.1646021.
[23] Flavio Chierichetti et al. “Event Detection via Communication Pattern Anal- ysis”. In: Proceedings of the Eighth International Conference on Weblogs and Social Media, ICWSM 2014, Ann Arbor, Michigan, USA, June 1-4, 2014.
2014. url: http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/ view/8088.
[24] Konstantina Chrysafiadi and Maria Virvou. “Review: Student Modeling Ap- proaches: A Literature Review for the Last Decade”. In: Expert Syst. Appl.
40.11 (Sept. 2013), pp. 4715–4729. issn: 0957-4174. doi: 10.1016/j.eswa. 2013.02.007. url: http://dx.doi.org/10.1016/j.eswa.2013.02.007. [25] Clyde H Coombs, John Edgar Milholland, and Frank Burton Womer. “The
assessment of partial knowledge”. In:Educational and Psychological Measure- ment 16.1 (1956), pp. 13–37.
[26] J. E. Corter et al. “Bugs and biases: Diagnosing misconceptions in the under- standing of diagrams”. In: Proceedings of the 31st Annual Conference of the
Cognitive Science Society. Ed. by N. A. Taatgen and H. van Rijn. Austin, TX:
[28] John S. Denker and Yann LeCun. “Transforming Neural-Net Output Levels to Probability Distributions”. In: NIPS. Morgan Kaufmann, 1990, pp. 853–859. [29] Marilu Dick-Perez et al. “A quantum chemistry concept inventory for physical
chemistry classes”. In: Journal of Chemical Education 93.4 (2016), pp. 605– 612.
[30] Jerome Epstein. “Development and validation of the Calculus Concept Inven- tory”. In: Proceedings of the ninth international conference on mathematics
education in a global community. Vol. 9. Charlotte, NC. 2007, pp. 165–170.
[31] Geir Evensen. “The Ensemble Kalman Filter: theoretical formulation and prac- tical implementation”. In: 53 (2003), pp. 343–367.doi:10.1007/s10236-003- 0036-9.
[32] Tristan Fletcher. “The Kalman Filter Explained”. 2010.
[33] Yarin Gal and Zoubin Ghahramani. “A Theoretically Grounded Application of Dropout in Recurrent Neural Networks”. In:Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing
Systems 2016, December 5-10, 2016, Barcelona, Spain. 2016, pp. 1019–1027.
[34] Yarin Gal and Zoubin Ghahramani. “Dropout As a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”. In:Proceedings of the 33rd International Conference on International Conference on Machine Learning -
Volume 48. ICML’16. New York, NY, USA: JMLR.org, 2016, pp. 1050–1059.
url:http://dl.acm.org/citation.cfm?id=3045390.3045502.
[35] Kathy Garvin-Doxas and Michael W Klymkowsky. “Understanding random- ness and its impact on student learning: lessons learned from building the Bi- ology Concept Inventory (BCI)”. In:CBE-Life Sciences Education 7.2 (2008), pp. 227–233.
[36] Arthur Gelb. “Applied Optimal Estimation”. In: The MIT Press, 1974.isbn: 0262570483, 9780262570480.
[37] Felix A. Gers, JÃijrgen Schmidhuber, and Fred Cummins. “Learning to For- get: Continual Prediction with LSTM”. In: Neural Computation 12 (1999), pp. 2451–2471.
[38] George Goguadze et al. “Evaluating a Bayesian Student Model of Decimal Misconceptions”. In:EDM. 2011.
[39] Alex Graves. “Generating Sequences With Recurrent Neural Networks.” In:
[40] Alex Graves. “Practical Variational Inference for Neural Networks”. In: Pro- ceedings of the 24th International Conference on Neural Information Process-
ing Systems. NIPS’11. Granada, Spain: Curran Associates Inc., 2011, pp. 2348–
2356.isbn: 978-1-61839-599-3.
[41] Gary L Gray et al. “The dynamics concept inventory assessment test: A progress report and some results”. In:American Society for Engineering Edu-
cation Annual Conference & Exposition. 2005.
[42] James H Hanson and Julia M Williams. “Using writing assignments to improve self-assessment and communication skills in an engineering statics course”. In:
Journal of engineering education 97.4 (2008), p. 515.
[43] Habibah Norehan Haron et al. “Self-regulated learning strategies between the performing and non-performing students in statics”. In:Interactive Collabora-
tive Learning (ICL), 2014 International Conference on. IEEE. 2014, pp. 802–
805.
[44] Eric L. Haseltine and James B. Rawlings. “Critical Evaluation of Extended Kalman Filtering and Moving-Horizon Estimation”. In: Industrial & Engi-
neering Chemistry Research 44.8 (June 2004), pp. 2451–2460. doi: 10.1021/
ie034308l.url: http://dx.doi.org/10.1021/ie034308l.
[45] José Miguel Hernández-Lobato and Ryan P. Adams. “Probabilistic Backprop- agation for Scalable Learning of Bayesian Neural Networks”. In:Proceedings of the 32Nd International Conference on International Conference on Machine
Learning - Volume 37. ICML’15. Lille, France: JMLR.org, 2015, pp. 1861–
1869.url: http://dl.acm.org/citation.cfm?id=3045118.3045316. [46] David Hestenes and Ibrahim Halloun. “Interpreting the force concept inven-
tory”. In:The Physics Teacher 33.8 (1995), pp. 502–506.
[47] David Hestenes, Malcolm Wells, Gregg Swackhamer, et al. “Force concept inventory”. In:The physics teacher 30.3 (1992), pp. 141–158.
[48] Randall W. Hill, Jr., and W. Lewis Johnson. “Designing an Intelligent Tutoring System for Database Modelling”. In: Proceedings of the world conference of
artificial intelligence in education. 1993, pp. 273–281.
[49] Geoffrey Hinton et al. “Improving neural networks by preventing co-adaptation of feature detectors”. In: CoRR abs/1207.0580 (2012). url: http://arxiv. org/abs/1207.0580.
Sixth Annual Conference on Computational Learning Theory. COLT ’93. Santa Cruz, California, USA: ACM, 1993, pp. 5–13. isbn: 0-89791-611-5. doi: 10. 1145/168304.168306.url: http://doi.acm.org/10.1145/168304.168306. [51] Sepp Hochreiter and Jürgen Schmidhuber. “Long Short-term Memory”. In:
Neural Comput. 9.9 (Nov. 1997), pp. 1735–1780. issn: 0899-7667. doi: 10 .
1162 / neco . 1997 . 9 . 8 . 1735. url: http : / / dx . doi . org / 10 . 1162 / neco . 1997.9.8.1735.
[52] Anneke Hommels, Akira Murakami, and Nishimura Shin-Ichi. “Comparison of the Ensemble Kalman filter with the Unscented Kalman filter: application to the construction of a road embankment”. In:Proceedings of the 19th European
Young Geotechnical Engineer Conference. Gyor, Hungary, 2009.
[53] Yuan Huang et al. “Understanding US regional linguistic variation with Twit- ter data analysis”. In: Computers, Environment and Urban Systems (2015).
issn: 0198-9715. url: http://www.sciencedirect.com/science/article/ pii/S0198971515300399.
[54] Douglas Huffman and Patricia Heller. “What Does the Force Concept Inven- tory Actually Measure?.” In:Physics Teacher 33.3 (1995), pp. 138–43.
[55] Jonathan Hurlock and Max L. Wilson. “Searching Twitter: Separating the Tweet from the Chaff.” In: ICWSM. Ed. by Lada A. Adamic, Ricardo A. Baeza-Yates, and Scott Counts. The AAAI Press, 2011.
[56] Tommi S. Jaakkola and Michael I. Jordan. “Bayesian parameter estimation via variational methods”. In: statistics and computing 10 (Jan. 2000), pp. 25–37. [57] Anthony Jacobi et al. “A concept inventory for heat transfer”. In: Frontiers
in Education, 2003. FIE 2003 33rd Annual. Vol. 1. IEEE. 2003, T3D–12.
[58] Bernard J. Jansen et al. “Twitter Power: Tweets As Electronic Word of Mouth”.
In:J. Am. Soc. Inf. Sci. Technol.60.11 (Nov. 2009), pp. 2169–2188.issn: 1532-
2882. doi: 10.1002/asi.v60:11. url: http://dx.doi.org/10.1002/asi. v60:11.
[59] Akshay Java et al. “Why We Twitter: Understanding Microblogging Usage and Communities”. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007
Workshop on Web Mining and Social Network Analysis. WebKDD/SNA-KDD
’07. San Jose, California: ACM, 2007, pp. 56–65.isbn: 978-1-59593-848-0.doi:
10.1145/1348549.1348556. url: http://doi.acm.org/10.1145/1348549. 1348556.
[60] Andrew H. Jazwinski. “Stochastic processes and filtering theory”. In: Mathe- matics in science and engineering 64. New York, NY [u.a.]: Acad. Press, 1970.
isbn: 0123815509.
[61] Finn V. Jensen and Thomas D. Nielsen. Bayesian Networks and Decision
Graphs. 2nd. Springer Publishing Company, Incorporated, 2007.
[62] Simon J. Julier and Jeffrey K. Uhlmann. “Unscented Filtering and Nonlinear Estimation”. In: PROCEEDINGS OF THE IEEE. 2004, pp. 401–422.
[63] Pamela Kalas et al. “Development of a meiosis concept inventory”. In: CBE-
Life Sciences Education 12.4 (2013), pp. 655–664.
[64] Andrej Karpathy and Li Fei-Fei. “Deep Visual-Semantic Alignments for Gen- erating Image Descriptions”. In: IEEE Trans. Pattern Anal. Mach. Intell.
39.4 (Apr. 2017), pp. 664–676. issn: 0162-8828. doi: 10.1109/TPAMI.2016. 2598339. url: https://doi.org/10.1109/TPAMI.2016.2598339.
[65] Matthias Katzfuss, Jonathan R. Stroud, and Christopher K. Wikle. “Under- standing the Ensemble Kalman Filter”. In: The American Statistician 70.4 (2016), pp. 350–357.doi:10.1080/00031305.2016.1141709.
[66] Yoon Kim et al. “Character-aware Neural Language Models”. In: Proceed-
ings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI’16.
Phoenix, Arizona: AAAI Press, 2016, pp. 2741–2749. url: http://dl.acm. org/citation.cfm?id=3016100.3016285.
[67] Duane Knudson et al. “Development and evaluation of a biomechanics concept inventory”. In:Sports Biomechanics 2.2 (2003), pp. 267–277.
[68] Fantian Kong et al. “Mobile Robot Localization Based on Extended Kalman Filter”. In: 2006 6th World Congress on Intelligent Control and Automation. Vol. 2. 2006, pp. 9242–9246.doi: 10.1109/WCICA.2006.1713789.
[69] Stephen Krause et al. “Development, testing, and application of a chemistry concept inventory”. In:Frontiers in Education, 2004. FIE 2004. 34th Annual. IEEE. 2004, T1G–1.
[70] John Krumm and Eric Horvitz. “Eyewitness: Identifying Local Events via Space-time Signals in Twitter Feeds”. In: Proceedings of the 23rd SIGSPA- TIAL International Conference on Advances in Geographic Information Sys- tems. GIS ’15. Bellevue, Washington: ACM, 2015, 20:1–20:10. isbn: 978-1-
[71] Haewoon Kwak et al. “What is Twitter, a Social Network or a News Media?”
In: Proceedings of the 19th International Conference on World Wide Web.
WWW ’10. Raleigh, North Carolina, USA: ACM, 2010, pp. 591–600. isbn: 978-1-60558-799-8. doi: 10.1145/1772690.1772751. url: http://doi.acm. org/10.1145/1772690.1772751.
[72] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. “Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles”. In:
Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach,
CA, USA. 2017, pp. 6405–6416. url:http://papers.nips.cc/paper/7219-
simple - and - scalable - predictive - uncertainty - estimation - using - deep-ensembles.
[73] Norm G. Lederman et al. “Views of nature of science questionnaire: Toward valid and meaningful assessment of learners’ conceptions of nature of science”.
In: Journal of Research in Science Teaching 39.6 (2002), pp. 497–521. issn:
1098-2736. doi: 10.1002/tea.10034. url: http://dx.doi.org/10.1002/ tea.10034.
[74] Kathy Lee et al. “Adverse Drug Event Detection in Tweets with Semi-Supervised Convolutional Neural Networks”. In: Proceedings of the 26th International
Conference on World Wide Web. WWW ’17. Perth, Australia: International
World Wide Web Conferences Steering Committee, 2017, pp. 705–714. isbn: 978-1-4503-4913-0.
[75] Kyumin Lee, Brian David Eoff, and James Caverlee. “Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter.” In:ICWSM. Ed. by Lada A. Adamic, Ricardo A. Baeza-Yates, and Scott Counts. The AAAI Press, 2011. url: http://dblp.uni-trier.de/db/conf/icwsm/icwsm2011. html#LeeEC11.
[76] Ryong Lee, Shoko Wakamiya, and Kazutoshi Sumiya. “Discovery of Unusual Regional Social Activities Using Geo-tagged Microblogs”. In:World Wide Web
14.4 (July 2011), pp. 321–349.issn: 1386-145X.
[77] Richard B Lewis. “Creative Teaching and Learning in a Statics Class.” In:
Engineering Education 81.1 (1991), pp. 15–18.
[78] Rui Li et al. “TEDAS: A Twitter-based Event Detection and Analysis System”.
In:Proceedings of the 2012 IEEE 28th International Conference on Data En-
gineering. ICDE ’12. Washington, DC, USA: IEEE Computer Society, 2012,
pp. 1273–1276. isbn: 978-0-7695-4747-3. doi:10.1109/ICDE.2012.125. url:
[79] Julie C Libarkin and Steven W Anderson. “Development of the geoscience concept inventory”. In: Proceedings of the National STEM Assessment Con-
ference, Washington DC. 2006, pp. 148–158.
[80] Xiao Lin and Gabriel Terejanu. “Fast Approximate Data Assimilation for High-Dimensional Problems”. In: 2017.url:https://arxiv.org/abs/1708. 02340.
[81] Thomas A Litzinger et al. “A cognitive study of problem solving in statics”.
In:Journal of Engineering Education 99.4 (2010), pp. 337–353.
[82] Ran Liu, Rony Patel, and Kenneth R. Koedinger. “Modeling Common Miscon- ceptions in Learning Process Data”. In:Proceedings of the Sixth International
Conference on Learning Analytics & Knowledge. LAK ’16. Edinburgh, United
Kingdom: ACM, 2016, pp. 369–377.isbn: 978-1-4503-4190-5. doi: 10.1145/ 2883851.2883967. url:http://doi.acm.org/10.1145/2883851.2883967. [83] David J. C. MacKay. “A Practical Bayesian Framework for Backpropagation
Networks”. In:Neural Comput.4.3 (May 1992), pp. 448–472. issn: 0899-7667.
doi: 10.1162/neco.1992.4.3.448. url: http://dx.doi.org/10.1162/ neco.1992.4.3.448.
[84] GermÃąn Kruszewski Marco Baroni, Georgiana Dinu. “Don’t count, predict! A systematic comparison of context-counting vs. context-predicting seman- tic vectors”. In: 52nd Annual Meeting of the Association for Computational
Linguistics, ACL 2014 - Proceedings of the Conference 1 (2014), pp. 238–247.
[85] A. Marcus et al. “TwitInfo: Aggregating and visualizing microblogs for event exploration”. In:Proceedings of the 2011 annual conference on Human factors
in computing systems. ACM. 2011, pp. 227–236.
[86] Adam Marcus et al. “Processing and Visualizing the Data in Tweets”. In:
SIGMOD Record 40.4 (Dec. 2011), pp. 21–27.
[87] Dimitris Margaritis. “Learning Bayesian Network Model Structure From Data”. PhD thesis. School of Computer Science, Carnegie-Mellon University, 2003. [88] Jay Martin, John Mitchell, and Ty Newell. “Development of a concept inven-
tory for fluid mechanics”. In: Frontiers in Education, 2003. FIE 2003 33rd
Annual. Vol. 1. IEEE. 2003, T3D–23.
[90] Michael Mathioudakis and Nick Koudas. “TwitterMonitor: Trend Detection over the Twitter Stream”. In: Proceedings of the 2010 ACM SIGMOD In-
ternational Conference on Management of Data. SIGMOD ’10. Indianapo-
lis, Indiana, USA: ACM, 2010, pp. 1155–1158. isbn: 978-1-4503-0032-2. doi:
10.1145/1807167.1807306. url: http://doi.acm.org/10.1145/1807167. 1807306.
[91] Polykarpos Meladianos et al. “Degeneracy-Based Real-Time Sub-Event Detec- tion in Twitter Stream.” In:ICWSM. Ed. by Meeyoung Cha, Cecilia Mascolo, and Christian Sandvig. AAAI Press, 2015, pp. 248–257. isbn: 978-1-57735- 733-9.
[92] K Clark Midkiff, Thomas A Litzinger, and DL Evans. “Development of en- gineering thermodynamics concept inventory instruments”. In: Frontiers in
Education Conference, 2001. 31st Annual. Vol. 2. IEEE. 2001, F2A–F23.
[93] Eva MillÃąn and JosÃľ-Luis PÃľrez de-la Cruz. “A Bayesian Diagnostic Algo- rithm for Student Modeling and its Evaluation.” In:User Model. User-Adapt.
Interact. 12.2-3 (2002), pp. 281–330.
[94] Multiple-Choice Test Preparation Manual.
[95] Mor Naaman, Hila Becker, and Luis Gravano. “Hip and trendy: Characterizing emerging trends on Twitter”. In: JASIST 62.5 (2011), pp. 902–918.
[96] Radford M. Neal.Bayesian Learning for Neural Networks. Secaucus, NJ, USA: Springer-Verlag New York, Inc., 1996. isbn: 0387947248.
[97] Radford M. Neal and Geoffrey E. Hinton. “Learning in Graphical Models”. In: ed. by Michael I. Jordan. Cambridge, MA, USA: MIT Press, 1999. Chap. A View of the EM Algorithm That Justifies Incremental, Sparse, and Other Vari- ants, pp. 355–368.isbn: 0-262-60032-3. url:http://dl.acm.org/citation. cfm?id=308574.308679.
[98] Jeffrey L Newcomer. “Inconsistencies in students’ approaches to solving prob- lems in Engineering Statics”. In:2010 IEEE Frontiers in Education Conference
(FIE). IEEE. 2010, F3G–1.
[99] Jeffrey L Newcomer and Paul S Steif. “Student explanations of answers to concept questions as a window into prior misconceptions”. In: Proceedings.
Frontiers in Education. 36th Annual Conference. IEEE. 2006, pp. 6–11.
[100] Jeffrey L Newcomer and Paul S Steif. “Student thinking about static equilib- rium: Insights from written explanations to a concept question”. In: Journal
[101] Jeffrey Nichols, Jalal Mahmud, and Clemens Drews. “Summarizing Sporting Events Using Twitter”. In: Proceedings of the 2012 ACM International Con-
ference on Intelligent User Interfaces. IUI ’12. Lisbon, Portugal: ACM, 2012,
pp. 189–198. isbn: 978-1-4503-1048-2. doi:10.1145/2166966.2166999. url:
http://doi.acm.org/10.1145/2166966.2166999.
[102] Branislav M Notaros. “Concept inventory assessment instruments for elec- tromagnetics education”. In:Antennas and Propagation Society International
Symposium, 2002. IEEE. Vol. 1. IEEE. 2002, pp. 684–687.
[103] Brendan O’Connor, Michel Krieger, and David Ahn. “TweetMotif: Exploratory Search and Topic Summarization for Twitter.” In:ICWSM. Ed. by William W. Cohen and Samuel Gosling. The AAAI Press, 2010. url:http://dblp.uni- trier.de/db/conf/icwsm/icwsm2010.html#OConnorKA10.
[104] Tokunbo Ogunfunmi and Mahmudur Rahman. “A concept inventory for an