Scientific Information Understanding via Open Educational Resources (OER)

Scientific publication retrieval/recommendation has been investigated in the past decade. However, to the best of our knowledge, few efforts have been made to help junior scholars and graduate students to understand and consume the essence of those scientific readings. This paper proposes a novel learning/reading environment, OER-based Collaborative PDF Reader (OCPR), that incorporates innovative scaffolding methods that can: 1. auto-characterize student emerging information need while reading a paper; and 2. enable students to readily access open educational resources (OER) based on their information need. By using metasearch methods, we pre-indexed 1,112,718 OERs, including presentation videos, slides, algorithm source code, or Wikipedia pages, for 41,378 STEM publications. Based on the computational information need, we use text mining and heterogeneous graph mining algorithms to recommend high quality OERs to help students better understand the scientific content in the paper. Evaluation results and exit surveys for an information retrieval course show that the OCPR system alone with the recommended OERs can effectively assist graduate students better understand the complex STEM publications. For instance, 78.42% of participants believe the OCPR system and recommended OERs can provide precise and useful information they need, while 78.43% of them believe the recommended OERs are close to exactly what they need when reading the paper. From OER ranking viewpoint, MRR, MAP and NDCG results prove that learning to rank and cold start solutions can efficiently integrate different text and graph ranking features.

[1]  J. Bruner,et al.  The role of tutoring in problem solving. , 1976, Journal of child psychology and psychiatry, and allied disciplines.

[2]  Xiaozhong Liu.  Generating metadata for cyberlearning resources through information retrieval and meta-search , 2013, J. Assoc. Inf. Sci. Technol..

[3]  W. Bruce Croft,et al.  A Language Modeling Approach to Information Retrieval , 1998, SIGIR Forum.

[4]  Yizhou Sun,et al.  Personalized entity recommendation: a heterogeneous information network approach , 2014, WSDM.

[5]  Elena Novak,et al.  The educational use of social annotation tools in higher education: A literature review , 2012, Internet High. Educ..

[6]  Moni Naor,et al.  Rank aggregation methods for the Web , 2001, WWW '01.

[7]  Charu C. Aggarwal,et al.  Co-author Relationship Prediction in Heterogeneous Bibliographic Networks , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.

[8]  Joanna Wolfe,et al.  Annotations and the collaborative digital library: Effects of an aligned annotation interface on student argumentation and reading strategies , 2008, Int. J. Comput. Support. Collab. Learn..

[9]  S. Puntambekar,et al.  Tools for Scaffolding Students in a Complex Learning Environment: What Have We Gained and What Have We Missed? , 2005 .

[10]  Ni Lao,et al.  Relational retrieval using a combination of path-constrained random walks , 2010, Machine Learning.

[11]  Jia Zhang,et al.  A Web 2.0-based collaborative annotation system for enhancing knowledge sharing in collaborative learning environments , 2010, Comput. Educ..

[12]  John D. Lafferty,et al.  A study of smoothing methods for language models applied to Ad Hoc information retrieval , 2001, SIGIR '01.

[13]  Philip S. Yu,et al.  Integrating meta-path selection with user-guided object clustering in heterogeneous information networks , 2012, KDD.

[14]  W. Bruce Croft,et al.  Linear feature-based models for information retrieval , 2007, Information Retrieval.

[15]  Xiaozhong Liu,et al.  Answering academic questions for education by recommending cyberlearning resources , 2013, J. Assoc. Inf. Sci. Technol..

[16]  Shankar Kumar,et al.  Video suggestion and discovery for youtube: taking random walks through the view graph , 2008, WWW.

[17]  Roy D. Pea,et al.  The Social and Technological Dimensions of Scaffolding and Related Theoretical Concepts for Learning, Education, and Human Activity , 2004, The Journal of the Learning Sciences.

[18]  Padhraic Smyth,et al.  Algorithms for estimating relative importance in networks , 2003, KDD '03.

[19]  Ramesh Nallapati,et al.  Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora , 2009, EMNLP.

[20]  Yizhou Sun,et al.  Meta-Path-Based Ranking with Pseudo Relevance Feedback on Heterogeneous Graph for Citation Recommendation , 2014, CIKM.

[21]  Tristan E. Johnson,et al.  Individual and team annotation effects on students' reading comprehension, critical thinking, and meta-cognitive skills , 2010, Comput. Hum. Behav..

[22]  Jian Qin,et al.  An interactive metadata model for structural, descriptive, and referential representation of scholarly output , 2014, J. Assoc. Inf. Sci. Technol..

[23]  Alan R. Dennis,et al.  Improving Learning with eTextbooks , 2015, 2015 48th Hawaii International Conference on System Sciences.

[24]  Yizhou Sun,et al.  Recommendation in heterogeneous information networks with implicit user feedback , 2013, RecSys.

[25]  Charu C. Aggarwal,et al.  When will it happen?: relationship prediction in heterogeneous information networks , 2012, WSDM '12.

[26]  Javed A. Aslam,et al.  Models for metasearch , 2001, SIGIR '01.

[27]  Yu He,et al.  The YouTube video recommendation system , 2010, RecSys '10.

[28]  Roy,et al.  The Social and Technological Dimensions of Scaffolding and Related Theoretical Concepts for Learning , Education , and Human Activity , 2004 .

[29]  Philip S. Yu,et al.  PathSim , 2011, Proc. VLDB Endow..