A factorization approach to evaluate open-response assignments in MOOCs using preference learning on peer assessments

We approach the problem of assessing open-text questions in MOOCs by peer-assessment.Our method avoids the intrinsic subjectivity of the numeric grades given by graders.Experiments where made with real-world data collected from 3 Universities in Spain.Our method performs well when comparing discrepancies among instructors' grades. Evaluating open-response assignments in Massive Open Online Courses is a difficult task because of the huge number of students involved. Peer grading is an effective method to address this problem. There are two basic approaches in the literature: cardinal and ordinal. The first case uses grades assigned by student-graders to a set of assignments of other colleagues. In the ordinal approach, the raw materials used by grading systems are the relative orders that graders appreciate in the assignments that they evaluate. In this paper we present a factorization method that seeks a trade-off between cardinal and ordinal approaches. The algorithm learns from preference judgments to avoid the subjectivity of the numeric grades. But in addition to preferences expressed by student-graders, we include other preferences: those induced from assignments with significantly different average grades. The paper includes a report of the results obtained using this approach in a real world dataset collected in 3 Universities of Spain, A Coruna, Pablo de Olavide at Sevilla, and Oviedo at Gijon. Additionally, we studied the sensitivity of the method with respect to the number of assignments graded by each student. Our method achieves similar or better scores than staff instructors when we measure the discrepancies with other instructor's grades.

[1]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[2]  Klaus Obermayer,et al.  Support vector learning for ordinal regression , 1999 .

[3]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[4]  Zhenghao Chen,et al.  Tuned Models of Peer Assessment in MOOCs , 2013, EDM.

[5]  V. Aggarwal Principles for using Machine Learning in the Assessment of Open Response Items: Programming Assessment as a Case Study , 2013 .

[6]  Siu Cheung Hui,et al.  Automatic summary assessment for intelligent tutoring systems , 2009, Comput. Educ..

[7]  Kannan Ramchandran,et al.  A Case for Ordinal Peer-evaluation in MOOCs , 2013 .

[8]  Carlo Strapparava,et al.  Automatic Assessment of Students' Free-Text Answers Underpinned by the Combination of a BLEU-Inspired Algorithm and Latent Semantic Analysis , 2005, FLAIRS Conference.

[9]  José Ramón Quevedo,et al.  Feature subset selection for learning preferences: a case study , 2004, ICML.

[10]  M. Mitchell Waldrop,et al.  Education online: The virtual lab , 2013, Nature.

[11]  Witold Pedrycz,et al.  Unsupervised feature selection via maximum projection and minimum redundancy , 2015, Knowl. Based Syst..

[12]  Ying-Ming Wang,et al.  Fuzzy preference relations: Aggregation and weight determination , 2007, Comput. Ind. Eng..

[13]  Paulo Oliveira,et al.  A system for formative assessment and monitoring of students' progress , 2014, Comput. Educ..

[14]  Nihar B. Shah Some Scaling Laws for MOOC Assessments , 2014 .

[15]  Salim Roukos,et al.  Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.

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

[17]  Thorsten Joachims,et al.  Methods for ordinal peer grading , 2014, KDD.

[18]  José Ramón Quevedo,et al.  Discovering Relevancies in Very Difficult Regression Problems: Applications to Sensory Data Analysis , 2004, ECAI.

[19]  Qingsheng Zhu,et al.  Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization , 2012, Knowl. Based Syst..

[20]  Fakhroddin Noorbehbahani,et al.  The automatic assessment of free text answers using a modified BLEU algorithm , 2011, Comput. Educ..

[21]  P. Sadler,et al.  The Impact of Self- and Peer-Grading on Student Learning , 2006 .

[22]  Cristina Conati,et al.  AIspace : Interactive Tools for Learning Artificial Intelligence , 2008 .

[23]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[24]  Jorge Dı́ez Peer Assessment in MOOCs Using Preference Learning via Matrix Factorization , 2013 .

[25]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[26]  Jason Weston,et al.  Large scale image annotation: learning to rank with joint word-image embeddings , 2010, Machine Learning.

[27]  William Barnett,et al.  The modern theory of consumer behavior: Ordinal or cardinal? , 2003 .