UpGrade: Sourcing Student Open-Ended Solutions to Create Scalable Learning Opportunities

In schools and colleges around the world, open-ended home-work assignments are commonly used. However, such assignments require substantial instructor effort for grading, and tend not to support opportunities for repeated practice. We propose UpGrade, a novel learnersourcing approach that generates scalable learning opportunities using prior student solutions to open-ended problems. UpGrade creates interactive questions that offer automated and real-time feedback, while enabling repeated practice. In a two-week experiment in a college-level HCI course, students answering UpGrade-created questions instead of traditional open-ended assignments achieved indistinguishable learning outcomes in ~30% less time. Further, no manual grading effort is required. To enhance quality control, UpGrade incorporates a psychometric approach using crowd workers' answers to automatically prune out low quality questions, resulting in a question bank that exceeds reliability standards for classroom use.

[1]  Larry Ambrose,et al.  The power of feedback. , 2002, Healthcare executive.

[2]  M. Tavakol,et al.  Making sense of Cronbach's alpha , 2011, International journal of medical education.

[3]  Vincent Aleven,et al.  A New Paradigm for Intelligent Tutoring Systems: Example-Tracing Tutors , 2009, Int. J. Artif. Intell. Educ..

[4]  Paul De Boeck,et al.  Descriptive and explanatory item response models , 2004 .

[5]  Krzysztof Z. Gajos,et al.  Crowdsourcing step-by-step information extraction to enhance existing how-to videos , 2014, CHI.

[6]  Justin Cheng,et al.  Peer and self assessment in massive online classes , 2013, ACM Trans. Comput. Hum. Interact..

[7]  Marsha C. Lovett,et al.  How learning works , 2010 .

[8]  Robert J. Crutcher,et al.  The role of deliberate practice in the acquisition of expert performance. , 1993 .

[9]  Krzysztof Z. Gajos,et al.  Data-driven interaction techniques for improving navigation of educational videos , 2014, UIST.

[10]  Juho Kim,et al.  ConceptScape: Collaborative Concept Mapping for Video Learning , 2018, CHI.

[11]  Juho Kim,et al.  Improving learning with collective learner activity , 2015 .

[12]  J. Sweller The worked example effect and human cognition , 2006 .

[13]  Deborah J. Harris Comparison of 1-, 2-, and 3-Parameter IRT Models , 1989 .

[14]  Neil T. Heffernan,et al.  The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching , 2014, International Journal of Artificial Intelligence in Education.

[15]  S. Chaiklin The zone of proximal development in Vygotsky's analysis of learning and instruction. , 2003 .

[16]  A. Kluger,et al.  The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. , 1996 .

[17]  Neil T. Heffernan,et al.  AXIS: Generating Explanations at Scale with Learnersourcing and Machine Learning , 2016, L@S.

[18]  F. Paas,et al.  Cognitive Load Theory and Instructional Design: Recent Developments , 2003 .

[19]  Krzysztof Z. Gajos,et al.  Learnersourcing Subgoal Labels for How-to Videos Citation , 2014 .

[20]  L. Cronbach Coefficient alpha and the internal structure of tests , 1951 .

[21]  Elena L. Glassman,et al.  RIMES: Embedding Interactive Multimedia Exercises in Lecture Videos , 2015, CHI.