Providing automated grading and personalized feedback

Practicing helps students reinforce the concepts that they have learned. It is equally important that personalized feedback are given to the students to help the student to learn from their mistakes. Traditionally, marking assessments and providing personalized feedback are carried out manually by teachers. However, both of these tasks become increasingly difficult as class size becomes large. To cope with this problem, we utilize natural language processing and machine learning techniques to mark essay type assessments and to help teachers provide personalized feedback promptly. The scheme determines the correctness of students' answers according to the meaning of the answers instead of comparing the words in the answers with the ones in the specimen answer. The scheme partitions the students' answers into groups according to their semantic meanings. Teachers provide feedback to a few answers in each group, and this feedback covers the typical issues demonstrated by the answers in the group. The feedback is propagated to the other answers in the same group. The proposed schemes showed reasonable marking accuracy.

[1]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[2]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[3]  Sanjeev Arora,et al.  A Simple but Tough-to-Beat Baseline for Sentence Embeddings , 2017, ICLR.

[4]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[5]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[6]  Carlos R. Rivero,et al.  Automated Personalized Feedback in Introductory Java Programming MOOCs , 2017, 2017 IEEE 33rd International Conference on Data Engineering (ICDE).

[7]  Philip J. Guo.  Codeopticon: Real-Time, One-To-Many Human Tutoring for Computer Programming , 2015, UIST.

[8]  Michael S. Bernstein,et al.  PeerStudio: Rapid Peer Feedback Emphasizes Revision and Improves Performance , 2015, L@S.

[9]  Sathiamoorthy Manoharan,et al.  Cheat-resistant multiple-choice examinations using personalization , 2019, Comput. Educ..

[10]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[11]  Elena L. Glassman,et al.  Learnersourcing Personalized Hints , 2016, CSCW.

[12]  Sathiamoorthy Manoharan,et al.  Machine Learning Techniques to Automate Scoring of Constructed-Response Type Assessments , 2018, 2018 28th EAEEIE Annual Conference (EAEEIE).

[13]  Philip J. Guo,et al.  OverCode: visualizing variation in student solutions to programming problems at scale , 2014, ACM Trans. Comput. Hum. Interact..

[14]  Christopher D. Manning,et al.  Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks , 2015, ACL.

[15]  Sathiamoorthy Manoharan,et al.  Personalized Assessment as a Means to Mitigate Plagiarism , 2017, IEEE Transactions on Education.

[16]  Björn Hartmann,et al.  Writing Reusable Code Feedback at Scale with Mixed-Initiative Program Synthesis , 2017, L@S.

[17]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[18]  Yiming Yang,et al.  XLNet: Generalized Autoregressive Pretraining for Language Understanding , 2019, NeurIPS.

[19]  Kevin Gimpel,et al.  Towards Universal Paraphrastic Sentence Embeddings , 2015, ICLR.