Forums, Feedback, and Two Kinds of AI: A Selective History of Learning @ Scale
Authors: 
Alex S. Duncan, 
Travis Tang, Yinghong Huang, Jeanette Luu, Nirali Thakkar, David A. JoynerAuthors Info & Claims
Alex S. Duncan, Travis Tang, Yinghong Huang, Jeanette Luu, Nirali Thakkar, David A. JoynerAuthors Info & ClaimsPages 376 - 382
Abstract
Since the beginning of the Learning @ Scale conference in 2014, research has focused on a variety of topics. In this paper, we look at trends in four specific topics over time to identify how focus on these research directions has changed over the past 10 years: discussion forums, AI and machine learning, accessibility and inclusivity, and peer review. We find that interest in discussion forums has remained relatively steady, while interest in artificial intelligence and accessibility & inclusivity has risen. Interest in peer review, by contrast, has waned considerably. These findings are based on a an analysis of all 562 total Learning @ Scale papers from 2014 through 2023, including full papers, works in progress, and short papers.
References
[1]
Kabir Abdulmajeed, David A. Joyner, and Christine McManus. 2020. Challenges of Online Learning in Nigeria. In Proceedings of the Seventh ACM Conference on Learning @ Scale (Virtual Event, USA) (L@S '20). Association for Computing Machinery, New York, NY, USA, 417--420. https://doi.org/10.1145/3386527.3405953
[2]
Vincent Aleven, Ryan Baker, Yuan Wang, Jonathan Sewall, and Octav Popescu. 2016. Bringing Non-programmer Authoring of Intelligent Tutors to MOOCs. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (Edinburgh, Scotland, UK) (L@S '16). Association for Computing Machinery, New York, NY, USA, 313--316. https://doi.org/10.1145/2876034.2893442
[3]
Raghav Apoorv, Akshay Dahiya, Uma Sreeram, Bharat Rahuldhev Patil, India Irish, Rocko Graziano, and Thad Starner. 2020. Examinator: A Plagiarism Detection Tool for Take-Home Exams. In Proceedings of the Seventh ACM Conference on Learning @ Scale (Virtual Event, USA) (L@S '20). Association for Computing Machinery, New York, NY, USA, 261--264. https://doi.org/10.1145/3386527.3406723
[4]
Kwamena Appiah-Kubi and Duncan Rowland. 2016. PEER Support In MOOCs: The Role Of Social Presence. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (Edinburgh, Scotland, UK) (L@S '16). Association for Computing Machinery, New York, NY, USA, 237--240. https://doi.org/10.1145/2876034.2893423
[5]
Jiye Baek and Jesse Shore. 2016. Promoting Student Engagement in MOOCs. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (Edinburgh, Scotland, UK) (L@S '16). Association for Computing Machinery, New York, NY, USA, 293--296. https://doi.org/10.1145/2876034.2893437
[6]
J Beaubien, L Stahl, R Herter, and D Paunesku. 2016. Promoting learning mindsets in schools: Lessons from educators' engagement with the PERTS Mindset Kit. The Project for Education Research That Scales (PERTS) (2016).
[7]
Jan Philip Bernius, Stephan Krusche, and Bernd Bruegge. 2021. A Machine Learning Approach for Suggesting Feedback in Textual Exercises in Large Courses. In Proceedings of the Eighth ACM Conference on Learning @ Scale (Virtual Event, Germany) (L@S '21). Association for Computing Machinery, New York, NY, USA, 173--182. https://doi.org/10.1145/3430895.3460135
[8]
Noah Bilgrien, Roy Finkelberg, Chirag Tailor, India Irish, Girish Murali, Abhishek Mangal, Niklas Gustafsson, Sumedha Raman, Thad Starner, and Rosa Arriaga. 2019. PARQR: Augmenting the Piazza Online Forum to Better Support Degree Seeking Online Masters Students. In Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale (Chicago, IL, USA) (L@S '19). Association for Computing Machinery, New York, NY, USA, Article 48, 4 pages. https://doi.org/10.1145/3330430.3333662
[9]
Christopher Brooks, René F. Kizilcec, and Nia Dowell. 2020. Designing Inclusive Learning Environments. In Proceedings of the Seventh ACM Conference on Learning @ Scale (Virtual Event, USA) (L@S '20). Association for Computing Machinery, New York, NY, USA, 225--228. https://doi.org/10.1145/3386527.3405935
[10]
Jill Burstein, Nitin Madnani, John Sabatini, Dan McCaffrey, Kietha Biggers, and Kelsey Dreier. 2017. Generating Language Activities in Real-Time for English Learners using Language Muse. In Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale (Cambridge, Massachusetts, USA) (L@S '17). Association for Computing Machinery, New York, NY, USA, 213--215. https://doi.org/10.1145/3051457.3053988
[11]
Jeongmin Byun, Jungkook Park, and Alice Oh. 2020. Detecting Contract Cheaters in Online Programming Classes with Keystroke Dynamics. In Proceedings of the Seventh ACM Conference on Learning @ Scale (Virtual Event, USA) (L@S '20). Association for Computing Machinery, New York, NY, USA, 273--276. https://doi.org/10.1145/3386527.3406726
[12]
Edward Cutrell, Jacki O'Neill, Srinath Bala, B. Nitish, Andrew Cross, Nakull Gupta, Viraj Kumar, and William Thies. 2015. Blended Learning in Indian Colleges with Massively Empowered Classroom. In Proceedings of the Second (2015) ACM Conference on Learning @ Scale (Vancouver, BC, Canada) (L@S '15). Association for Computing Machinery, New York, NY, USA, 47--56. https://doi.org/10.1145/2724660.2724666
[13]
Ali Darvishi, Hassan Khosravi, Solmaz Abdi, Shazia Sadiq, and Dragan Gavsević. 2022. Incorporating Training, Self-monitoring and AI-Assistance to Improve Peer Feedback Quality. In Proceedings of the Ninth ACM Conference on Learning @ Scale (New York City, NY, USA) (L@S '22). Association for Computing Machinery, New York, NY, USA, 35--47. https://doi.org/10.1145/3491140.3528265
[14]
Ozgur Dedehayir and Martin Steinert. 2016. The hype cycle model: A review and future directions. Technological Forecasting and Social Change, Vol. 108 (2016), 28--41.
[15]
Dorottya Demszky and Jing Liu. 2023. M-Powering Teachers: Natural Language Processing Powered Feedback Improves 1:1 Instruction and Student Outcomes. In Proceedings of the Tenth ACM Conference on Learning @ Scale (Copenhagen, Denmark) (L@S '23). Association for Computing Machinery, New York, NY, USA, 59--69. https://doi.org/10.1145/3573051.3593379
[16]
Vijaya Dommeti and Douglas Selent. 2017. Applying and Exploring Bayesian Hypothesis Testing for Large Scale Experimentation in Online Tutoring Systems. In Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale (Cambridge, Massachusetts, USA) (L@S '17). Association for Computing Machinery, New York, NY, USA, 229--232. https://doi.org/10.1145/3051457.3053992
[17]
Nicholas Dronen, Peter W. Foltz, and Kyle Habermehl. 2015. Effective Sampling for Large-scale Automated Writing Evaluation Systems. In Proceedings of the Second (2015) ACM Conference on Learning @ Scale (Vancouver, BC, Canada) (L@S '15). Association for Computing Machinery, New York, NY, USA, 3--10. https://doi.org/10.1145/2724660.2724661
[18]
Alex Duncan and David Joyner. 2019. Peer Advising at Scale: Content and Context of a Learner-Owned Course Evaluation System. In Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale (Chicago, IL, USA) (L@S '19). Association for Computing Machinery, New York, NY, USA, Article 46, 4 pages. https://doi.org/10.1145/3330430.3333660
[19]
Alex Duncan, Ana Rusch, Prerna Ravi, and David Joyner. 2023. The L@St Eight Years: A Review of Papers and Authors at Learning @ Scale. In Proceedings of the Tenth ACM Conference on Learning @ Scale (L@S '23'). ACM, New York, NY. https://doi.org/10.1145/3573051.3596192
[20]
Carol S Dweck. 2013. Self-theories: Their role in motivation, personality, and development. Psychology press.
[21]
Bobbie Lynn Eicher and David Joyner. 2021. Components of assessments and grading at scale. In Proceedings of the Eighth ACM Conference on Learning@ Scale. 303--306.
[22]
Elaine Farrow, Myroslava O. Dzikovska, and Johanna D. Moore. 2016. Beetle-Grow: An Effective Intelligent Tutoring System for Data Collection. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (Edinburgh, Scotland, UK) (L@S '16). Association for Computing Machinery, New York, NY, USA, 177--180. https://doi.org/10.1145/2876034.2893408
[23]
Joseph L. Fleiss. 1971. Measuring nominal scale agreement among many raters. Psychological Bulletin, Vol. 76, 5 (1971), 378--382. https://doi.org/10.1037/h0031619
[24]
Chase Geigle and ChengXiang Zhai. 2016. Scaling up Online Question Answering via Similar Question Retrieval. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (Edinburgh, Scotland, UK) (L@S '16). Association for Computing Machinery, New York, NY, USA, 257--260. https://doi.org/10.1145/2876034.2893428
[25]
Chase Geigle, ChengXiang Zhai, and Duncan C. Ferguson. 2016. An Exploration of Automated Grading of Complex Assignments. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (Edinburgh, Scotland, UK) (L@S '16). Association for Computing Machinery, New York, NY, USA, 351--360. https://doi.org/10.1145/2876034.2876049
[26]
Rocko Graziano, David Benton, Sarthak Wahal, Qiuyue Xue, P. Tim Miller, Nick Larsen, Diego Vacanti, Pepper Miller, Khushhall Chandra Mahajan, Deepak Srikanth, and Thad Starner. 2019. Jack Watson: Addressing Contract Cheating at Scale in Online Computer Science Education. In Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale (Chicago, IL, USA) (L@S '19). Association for Computing Machinery, New York, NY, USA, Article 52, 4 pages. https://doi.org/10.1145/3330430.3333666
[27]
Andrew Head, Elena Glassman, Gustavo Soares, Ryo Suzuki, Lucas Figueredo, Loris D'Antoni, and Björn Hartmann. 2017. Writing Reusable Code Feedback at Scale with Mixed-Initiative Program Synthesis. In Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale (Cambridge, Massachusetts, USA) (L@S '17). Association for Computing Machinery, New York, NY, USA, 89--98. https://doi.org/10.1145/3051457.3051467
[28]
Thomas Hillman, Alena Seredko, Tanya Karin Osborne, and Markus Nivala. 2023. From Answering for Points to Commenting for Others. In Proceedings of the Tenth ACM Conference on Learning @ Scale (Copenhagen, Denmark) (L@S '23). Association for Computing Machinery, New York, NY, USA, 92--98. https://doi.org/10.1145/3573051.3593385
[29]
Jonathan Huang, Anirban Dasgupta, Arpita Ghosh, Jane Manning, and Marc Sanders. 2014. Superposter behavior in MOOC forums. In Proceedings of the First ACM Conference on Learning @ Scale Conference (Atlanta, Georgia, USA) (L@S '14). Association for Computing Machinery, New York, NY, USA, 117--126. https://doi.org/10.1145/2556325.2566249
[30]
Jui-Tse Hung, Christopher Cui, Varun Agarwal, Saurabh Chatterjee, Raghav Apoorv, Rocko Graziano, and Thad Starner. 2023. Examinator v3.0: Cheating Detection in Online Take-Home Exams. In Proceedings of the Tenth ACM Conference on Learning @ Scale (Copenhagen, Denmark) (L@S '23). Association for Computing Machinery, New York, NY, USA, 401--405. https://doi.org/10.1145/3573051.3596196
[31]
India Irish, Saurabh Chatterjee, Chirag Tailor, Roy Finkelberg, Rosa Arriaga, and Thad Starner. 2022. Post Recommendation System Impact on Student Participation and Performance in an Online AI Graduate Course. In Proceedings of the Ninth ACM Conference on Learning @ Scale (New York City, NY, USA) (L@S '22). Association for Computing Machinery, New York, NY, USA, 24--34. https://doi.org/10.1145/3491140.3528280
[32]
India Irish, Roy Finkelberg, Daniel Nkemelu, Swar Gujrania, Aadarsh Padiyath, Sumedha Raman, Chirag Tailor, Rosa Arriaga, and Thad Starner. 2020. PARQR: Automatic Post Suggestion in the Piazza Online Forum to Support Degree Seeking Online Masters Students. In Proceedings of the Seventh ACM Conference on Learning @ Scale (Virtual Event, USA) (L@S '20). Association for Computing Machinery, New York, NY, USA, 125--134. https://doi.org/10.1145/3386527.3405914
[33]
Petr Johanes. 2019. Start of a Science: An Epistemological Analysis of Learning at Scale. In Proceedings of the Sixth (2019) ACM Conference on Learning@ Scale. 1--10.
[34]
Benny G. Johnson, Jeffrey S. Dittel, Rachel Van Campenhout, and Bill Jerome. 2022. Discrimination of Automatically Generated Questions Used as Formative Practice. In Proceedings of the Ninth ACM Conference on Learning @ Scale (New York City, NY, USA) (L@S '22). Association for Computing Machinery, New York, NY, USA, 325--329. https://doi.org/10.1145/3491140.3528323
[35]
Katy Jordan and Christina Myers. 2022. EdTech and Girls Education in Low- and Middle-Income Countries: Which Intervention Types Have the Greatest Impact on Learning Outcomes for Girls?. In Proceedings of the Ninth ACM Conference on Learning @ Scale (L@S '22). ACM. https://doi.org/10.1145/3491140.3528305
[36]
David A Joyner. 2017. Scaling expert feedback: Two case studies. In Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale. 71--80.
[37]
Yunsung Kim and Chris Piech. 2023. High-Resolution Course Feedback: Timely Feedback Mechanism for Instructors. In Proceedings of the Tenth ACM Conference on Learning @ Scale (Copenhagen, Denmark) (L@S '23). Association for Computing Machinery, New York, NY, USA, 81--91. https://doi.org/10.1145/3573051.3593391
[38]
Christopher Kok and Xu Wang. 2023. Comb: Giving Feedback to Short Answer at Scale with Human-in-the-Loop Rubric Creation. In Proceedings of the Tenth ACM Conference on Learning @ Scale (Copenhagen, Denmark) (L@S '23). Association for Computing Machinery, New York, NY, USA, 398--400. https://doi.org/10.1145/3573051.3596195
[39]
Pushkar Kolhe, Michael L Littman, and Charles L Isbell. 2016. Peer reviewing short answers using comparative judgement. In Proceedings of the Third (2016) ACM Conference on Learning@ Scale. 241--244.
[40]
Sean Kross and Philip J Guo. 2018. Students, systems, and interactions: synthesizing the first four years of learning@ scale and charting the future. In Proceedings of the Fifth Annual ACM Conference on Learning at Scale. 1--10.
[41]
Chinmay Kulkarni. 2019. Design perspectives of learning at scale: scaling efficiency and empowerment. In Proceedings of the Sixth (2019) ACM Conference on Learning@ Scale. 1--11.
[42]
Chinmay E. Kulkarni, Richard Socher, Michael S. Bernstein, and Scott R. Klemmer. 2014. Scaling short-answer grading by combining peer assessment with algorithmic scoring. In Proceedings of the First ACM Conference on Learning @ Scale Conference (Atlanta, Georgia, USA) (L@S '14). Association for Computing Machinery, New York, NY, USA, 99--108. https://doi.org/10.1145/2556325.2566238
[43]
Saar Kuzi, William Cope, Duncan Ferguson, Chase Geigle, and ChengXiang Zhai. 2019. Automatic Assessment of Complex Assignments using Topic Models. In Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale (Chicago, IL, USA) (L@S '19). Association for Computing Machinery, New York, NY, USA, Article 13, 10 pages. https://doi.org/10.1145/3330430.3333615
[44]
Andrew S. Lan, Divyanshu Vats, Andrew E. Waters, and Richard G. Baraniuk. 2015. Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions. In Proceedings of the Second (2015) ACM Conference on Learning @ Scale (Vancouver, BC, Canada) (L@S '15). Association for Computing Machinery, New York, NY, USA, 167--176. https://doi.org/10.1145/2724660.2724664
[45]
Changyoon Lee, Donghoon Han, Hyoungwook Jin, and Alice Oh. 2019. automaTA: Human-Machine Interaction for Answering Context-Specific Questions. In Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale (Chicago, IL, USA) (L@S '19). Association for Computing Machinery, New York, NY, USA, Article 44, 4 pages. https://doi.org/10.1145/3330430.3333658
[46]
Hansol Lee, René F. Kizilcec, and Thorsten Joachims. 2023. Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions. In Proceedings of the Tenth ACM Conference on Learning @ Scale (Copenhagen, Denmark) (L@S '23). Association for Computing Machinery, New York, NY, USA, 195--203. https://doi.org/10.1145/3573051.3593382
[47]
Nicholas Lewkow, Jacqueline Feild, Neil Zimmerman, Mark Riedesel, Alfred Essa, David Boulanger, Jeremie Seanosky, Vive Kumar, Kinshuk, and Sandhya Kode. 2016. A Scalable Learning Analytics Platform for Automated Writing Feedback. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (Edinburgh, Scotland, UK) (L@S '16). Association for Computing Machinery, New York, NY, USA, 109--112. https://doi.org/10.1145/2876034.2893380
[48]
Min Lu, Kaori Tamura, Tsuyoshi Okamoto, Misato Oi, Atsushi Shimada, Kohei Hatano, Masanori Yamada, and Shin'ichi Konomi. 2019. Proposal and Implementation of an Elderly-oriented User Interface for Learning Support Systems. In Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale (Chicago, IL, USA) (L@S '19). Association for Computing Machinery, New York, NY, USA, Article 37, 4 pages. https://doi.org/10.1145/3330430.3333650
[49]
Michael Joseph Meaney. 2022. Designing and Building Inclusive, Entry-level Massive Open Online Courses (MOOCs): Perspectives from Producers. In Proceedings of the Ninth ACM Conference on Learning @ Scale (New York City, NY, USA) (L@S '22). Association for Computing Machinery, New York, NY, USA, 189--200. https://doi.org/10.1145/3491140.3528281
[50]
Vineet Pandey, Yasmine Kotturi, Chinmay Kulkarni, Michael S. Bernstein, and Scott Klemmer. 2015. Connecting Stories and Pedagogy Increases Participant Engagement in Discussions. In Proceedings of the Second (2015) ACM Conference on Learning @ Scale (Vancouver, BC, Canada) (L@S '15). Association for Computing Machinery, New York, NY, USA, 253--256. https://doi.org/10.1145/2724660.2728670
[51]
Mar Perez-Sanagustin, Ronald Pérez-Álvarez, Jorge Maldonado-Mahauad, Esteban Villalobos, Isabel Hilliger, Josefina Hernández, Diego Sapunar, Pedro Manuel Moreno-Marcos, Pedro J. Mu noz Merino, Carlos Delgado Kloos, and Jon Imaz. 2021. Can Feedback based on Predictive Data Improve Learners' Passing Rates in MOOCs? A Preliminary Analysis. In Proceedings of the Eighth ACM Conference on Learning @ Scale (Virtual Event, Germany) (L@S '21). Association for Computing Machinery, New York, NY, USA, 339--342. https://doi.org/10.1145/3430895.3460991
[52]
Trang Thi Pham. 2023. Digital Access, Usage, and Educational Outcomes: Evidence from Vietnam. In Proceedings of the Tenth ACM Conference on Learning @ Scale (Copenhagen, Denmark) (L@S '23). Association for Computing Machinery, New York, NY, USA, 267--271. https://doi.org/10.1145/3573051.3596169
[53]
Chris Piech, Mehran Sahami, Jonathan Huang, and Leonidas Guibas. 2015. Autonomously Generating Hints by Inferring Problem Solving Policies. In Proceedings of the Second (2015) ACM Conference on Learning @ Scale (Vancouver, BC, Canada) (L@S '15). Association for Computing Machinery, New York, NY, USA, 195--204. https://doi.org/10.1145/2724660.2724668
[54]
Nea Pirttinen, Juho Leinonen, Annemari Auvinen, Vesa Lappalainen, Katja Tynkkynen, Henrik Hedberg, Mikko-Jussi Laakso, and Kjell Lemström. 2022. Digital Education For All: Multi-University Study of Increasing Competent Student Admissions at Scale. In Proceedings of the Ninth ACM Conference on Learning @ Scale (New York City, NY, USA) (L@S '22). Association for Computing Machinery, New York, NY, USA, 72--81. https://doi.org/10.1145/3491140.3528266
[55]
Karthik Raman and Thorsten Joachims. 2015. Bayesian Ordinal Peer Grading. In Proceedings of the Second (2015) ACM Conference on Learning @ Scale (Vancouver, BC, Canada) (L@S '15). Association for Computing Machinery, New York, NY, USA, 149--156. https://doi.org/10.1145/2724660.2724678
[56]
Justin Reich, Brandon Stewart, Kimia Mavon, and Dustin Tingley. 2016. The Civic Mission of MOOCs: Measuring Engagement across Political Differences in Forums. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (Edinburgh, Scotland, UK) (L@S '16). Association for Computing Machinery, New York, NY, USA, 1--10. https://doi.org/10.1145/2876034.2876045
[57]
Robert Stanyon, Austin A. Tomlinson, Manjinder Kainth, and Nicola K. Wilkin. 2022. Providing Individual Student Feedback at Scale for Mathematical Disciplines. In Proceedings of the Ninth ACM Conference on Learning @ Scale (New York City, NY, USA) (L@S '22). Association for Computing Machinery, New York, NY, USA, 400--404. https://doi.org/10.1145/3491140.3528313
[58]
Sofia Strukova, José A. Ruipérez-Valiente, and Félix Gómez Mármol. 2023. Towards the Identification of Experts in Informal Learning Portals at Scale. In Proceedings of the Tenth ACM Conference on Learning @ Scale (Copenhagen, Denmark) (L@S '23). Association for Computing Machinery, New York, NY, USA, 316--320. https://doi.org/10.1145/3573051.3596179
[59]
Selen Türkay, Hadas Eidelman, Yigal Rosen, Daniel Seaton, Glenn Lopez, and Jacob Whitehill. 2017. Getting to Know English Language Learners in MOOCs: Their Motivations, Behaviors, and Outcomes. In Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale (Cambridge, Massachusetts, USA) (L@S '17). Association for Computing Machinery, New York, NY, USA, 209--212. https://doi.org/10.1145/3051457.3053987
[60]
Judith Uchidiuno, Jessica Hammer, Evelyn Yarzebinski, Kenneth R. Koedinger, and Amy Ogan. 2017. Characterizing ELL Students' Behavior During MOOC Videos Using Content Type. In Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale (Cambridge, Massachusetts, USA) (L@S '17). Association for Computing Machinery, New York, NY, USA, 185--188. https://doi.org/10.1145/3051457.3053981
[61]
Judith Uchidiuno, Amy Ogan, Kenneth R. Koedinger, Evelyn Yarzebinski, and Jessica Hammer. 2016. Browser Language Preferences as a Metric for Identifying ESL Speakers in MOOCs. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (Edinburgh, Scotland, UK) (L@S '16). Association for Computing Machinery, New York, NY, USA, 277--280. https://doi.org/10.1145/2876034.2893433
[62]
Judith Uchidiuno, Amy Ogan, Evelyn Yarzebinski, and Jessica Hammer. 2016. Understanding ESL Students' Motivations to Increase MOOC Accessibility. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (Edinburgh, Scotland, UK) (L@S '16). Association for Computing Machinery, New York, NY, USA, 169--172. https://doi.org/10.1145/2876034.2893398
[63]
Rachel Van Campenhout, Nick Brown, Bill Jerome, Jeffrey S. Dittel, and Benny G. Johnson. 2021. Toward Effective Courseware at Scale: Investigating Automatically Generated Questions as Formative Practice. In Proceedings of the Eighth ACM Conference on Learning @ Scale (Virtual Event, Germany) (L@S '21). Association for Computing Machinery, New York, NY, USA, 295--298. https://doi.org/10.1145/3430895.3460162
[64]
Aditya Vishwanath, Arkadeep Kumar, and Neha Kumar. 2016. Learning about Teaching in Low-Resource Indian Contexts. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (Edinburgh, Scotland, UK) (L@S '16). Association for Computing Machinery, New York, NY, USA, 305--308. https://doi.org/10.1145/2876034.2893440
[65]
Karen D. Wang, Shima Salehi, Max Arseneault, Krishnan Nair, and Carl Wieman. 2021. Automating the Assessment of Problem-solving Practices Using Log Data and Data Mining Techniques. In Proceedings of the Eighth ACM Conference on Learning @ Scale (Virtual Event, Germany) (L@S '21). Association for Computing Machinery, New York, NY, USA, 69--76. https://doi.org/10.1145/3430895.3460127
[66]
Zichao Wang, Andrew S. Lan, Weili Nie, Andrew E. Waters, Phillip J. Grimaldi, and Richard G. Baraniuk. 2018. QG-net: a data-driven question generation model for educational content. In Proceedings of the Fifth Annual ACM Conference on Learning at Scale (London, United Kingdom) (L@S '18). Association for Computing Machinery, New York, NY, USA, Article 7, 10 pages. https://doi.org/10.1145/3231644.3231654
[67]
Andrew E. Waters, David Tinapple, and Richard G. Baraniuk. 2015. BayesRank: A Bayesian Approach to Ranked Peer Grading. In Proceedings of the Second (2015) ACM Conference on Learning @ Scale (Vancouver, BC, Canada) (L@S '15). Association for Computing Machinery, New York, NY, USA, 177--183. https://doi.org/10.1145/2724660.2724672
[68]
Joseph Jay Williams, Juho Kim, Anna Rafferty, Samuel Maldonado, Krzysztof Z. Gajos, Walter S. Lasecki, and Neil Heffernan. 2016. AXIS: Generating Explanations at Scale with Learnersourcing and Machine Learning. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (Edinburgh, Scotland, UK) (L@S '16). Association for Computing Machinery, New York, NY, USA, 379--388. https://doi.org/10.1145/2876034.2876042
[69]
Angelica Willis, Glenn Davis, Sherry Ruan, Lakshmi Manoharan, James Landay, and Emma Brunskill. 2019. Key Phrase Extraction for Generating Educational Question-Answer Pairs. In Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale (Chicago, IL, USA) (L@S '19). Association for Computing Machinery, New York, NY, USA, Article 20, 10 pages. https://doi.org/10.1145/3330430.3333636
[70]
Ye Xiong and Yi-Fang Brook Wu. 2019. An Automated Feedback System to Support Student Learning in Writing-to-Learn Activities. In Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale (Chicago, IL, USA) (L@S '19). Association for Computing Machinery, New York, NY, USA, Article 43, 4 pages. https://doi.org/10.1145/3330430.3333657
[71]
David S Yeager, Carissa Romero, Dave Paunesku, Christopher S Hulleman, Barbara Schneider, Cintia Hinojosa, Hae Yeon Lee, Joseph O'Brien, Kate Flint, Alice Roberts, et al. 2016. Using design thinking to improve psychological interventions: The case of the growth mindset during the transition to high school. Journal of educational psychology, Vol. 108, 3 (2016), 374.
[72]
Eran Yogev, Kobi Gal, David Karger, Marc T. Facciotti, and Michele Igo. 2018. Classifying and visualizing students' cognitive engagement in course readings. In Proceedings of the Fifth Annual ACM Conference on Learning at Scale (London, United Kingdom) (L@S '18). Association for Computing Machinery, New York, NY, USA, Article 52, 10 pages. https://doi.org/10.1145/3231644.3231648
[73]
Amy X. Zhang, Michele Igo, Marc Facciotti, and David Karger. 2017. Using Student Annotated Hashtags and Emojis to Collect Nuanced Affective States. In Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale (Cambridge, Massachusetts, USA) (L@S '17). Association for Computing Machinery, New York, NY, USA, 319--322. https://doi.org/10.1145/3051457.3054014
[74]
Siyuan Zhao, Yaqiong Zhang, Xiaolu Xiong, Anthony Botelho, and Neil Heffernan. 2017. A Memory-Augmented Neural Model for Automated Grading. In Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale (Cambridge, Massachusetts, USA) (L@S '17). Association for Computing Machinery, New York, NY, USA, 189--192. https://doi.org/10.1145/3051457.3053982
[75]
Mo Zhou, Alison Cliff, Allen Huang, Sanjay Krishnan, Brandie Nonnecke, Kanji Uchino, Sam Joseph, Armando Fox, and Ken Goldberg. 2015. M-CAFE: Managing MOOC Student Feedback with Collaborative Filtering. In Proceedings of the Second (2015) ACM Conference on Learning @ Scale (Vancouver, BC, Canada) (L@S '15). Association for Computing Machinery, New York, NY, USA, 309--312. https://doi.org/10.1145/2724660.2728681
Index Terms
- Forums, Feedback, and Two Kinds of AI: A Selective History of Learning @ Scale
Recommendations
You Shall Know a Forum by the Words they Keep: Analyzing Language Use in Accessibility Forums for Blind Users
HT '24: Proceedings of the 35th ACM Conference on Hypertext and Social MediaDiscussion forums are one of the favored platforms for knowledge sharing. Given their popularity, copious research exists on understanding linguistic and behavioral characteristics of forum conversations. However, prior investigations have mainly ...
The Research on the Application of Artificial Intelligence in Education in China: A Systematic Review
Resilience and Future of Smart LearningAbstractThe rapid development of artificial intelligence and other emerging technologies has affected all fields of society. The combination of artificial intelligence and education has also received unprecedented attention. Under the promotion of ...
Talk amongst yourselves: inviting users to participate in online conversations
IUI '07: Proceedings of the 12th international conference on Intelligent user interfacesMany small online communities would benefit from increased diversity or activity in their membership. Some communities run the risk of dying out due to lack of participation. Others struggle to achieve the critical mass necessary for diverse and ...
Comments
Information & Contributors
Information
Published In
July 2024
582 pages
ISBN:9798400706332
DOI:10.1145/3657604
- General Chair:
- David Joyner,
- Program Chairs:
- Min Kyu Kim,
- Xu Wang,
- Meng Xia
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 15 July 2024
Check for updates
Author Tags
Qualifiers
- Short-paper
Conference
L@S '24
Acceptance Rates
Overall Acceptance Rate 117 of 440 submissions, 27%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 85Total Downloads
- Downloads (Last 12 months)85
- Downloads (Last 6 weeks)37
Reflects downloads up to 16 Oct 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in