Skip to main content
SpringerLink
Log in

Fostering parent–child dialog through automated discussion suggestions

  • Published:
User Modeling and User-Adapted Interaction Aims and scope Submit manuscript

Abstract

The development of early literacy skills has been critically linked to a child’s later academic success. In particular, repeated studies have shown that reading aloud to children and providing opportunities for them to discuss the stories that they hear is of utmost importance to later academic success. CloudPrimer is a tablet-based interactive reading primer that aims to foster early literacy skills by supporting parents in shared reading with their children through user-targeted discussion topic suggestions. The tablet application records discussions between parents and children as they read a story and, in combination with a common sense knowledge base, leverages this information to produce suggestions. Because of the unique challenges presented by our application, the suggestion generation method relies on a novel topic modeling method that is based on semantic graph topology. We conducted a user study in which we compared how delivering suggestions generated by our approach compares to expert-crafted suggestions. Our results show that our system can successfully improve engagement and parent–child reading practices in the absence of a literacy expert’s tutoring.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Aharony, N., Gardner, A., Sumter, C., Pentland, A.: Funf: open sensing framework (2011)

  • Alonso, J.B., Chang, A., Breazeal, C.: Values impacting the design of an adaptive educational storybook. In In International Conference on Interactive Digital Storytelling, pp. 350–353. Springer, Berlin (2011)

  • Arnold, D.S., Whitehurst, G.J.: Accelerating language development through picture book reading: A summary of dialogic reading and its effect. In: Bridges to literacy: Children, families, and schools, pp. 103–128 (1994)

  • Bennett, J., Lanning, S.: The netflix prize. In: Proceedings of KDD cup and workshop, vol. 2007, p. 35 (2007)

  • Bernstein, M.S., Brandt, J., Miller, R.C., Karger, D.R.: Crowds in two seconds: enabling realtime crowd-powered interfaces. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, ACM, pp. 33–42 (2011)

  • Bessho, F., Harada, T., Kuniyoshi, Y.: Dialog system using real-time crowdsourcing and twitter large-scale corpus. In: Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 227–231. Association for Computational Linguistics (2012)

  • Bird, S.: Nltk: the natural language toolkit. In: Proceedings of the COLING/ACL on Interactive Presentation Sessions, pp. 69–72. Association for Computational Linguistics (2006)

  • Blei, David M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)

    Article  MathSciNet  Google Scholar 

  • Blei, David M, Lafferty, John D: Topic models. Text Min. 10(71), 34 (2009)

    MathSciNet  Google Scholar 

  • Blewitt, Pamela, Rump, Keiran M., Shealy, Stephanie E., Cook, Samantha A.: Shared book reading: when and how questions affect young children’s word learning. J. Educ. Psychol. 101(2), 294 (2009)

    Article  Google Scholar 

  • Boteanu, A., Chernova, S.: Modeling discussion topics in interactions with a tablet reading primer. In: Proceedings of the 2013 International Conference on Intelligent User Interfaces, pp. 75–84. ACM (2013a)

  • Boteanu, A., Chernova, S.: Unsupervised rating prediction based on local and global semantic models. In: 2013 AAAI Fall Symposium Series (2013b)

  • Bouchard, Bruno, Bouzouane, Abdenour, Giroux, Sylvain: A smart home agent for plan recognition. In: Proceedings of the Fifth International Joint Conference on Autonomous Agents And Multiagent Systems, AAMAS ’06, pp. 320–322, New York, NY, USA, 2006. ACM. ISBN 1-59593-303-4. doi:10.1145/1160633.1160687

  • Boyd-Graber, J.L., Blei, D.M., Zhu, X.: A topic model for word sense disambiguation. In: EMNLP-CoNLL, pp. 1024–1033 (2007)

  • Burns, M.S., Griffin, P., Snows, C.E.: Starting out right. A guide promoting children’s reading success. wdc (1999)

  • Bus, Adriana G., Ijzendoorn, Marinus H.Van, Pellegrini, Anthony D.: Joint book reading makes for success in learning to read: a meta-analysis on intergenerational transmission of literacy. Rev. Educ. Res. 65(1), 1–21 (1995)

    Article  Google Scholar 

  • Cambria, E., Olsher, D., Rajagopal, D.: Senticnet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: Twenty-eighth AAAI conference on artificial intelligence (2014)

  • Chang, A., Breazeal, C., Faridi, F., Roberts, T., Davenport, G., Lieberman, H., and Montfort, N.: Textual tinkerability: encouraging storytelling behaviors to foster emergent literacy. In: CHI’12 Extended Abstracts on Human Factors in Computing Systems, pp. 505–520. ACM (2012)

  • Chang, A., Breazeal, C.: Tinkrbook: shared reading interfaces for storytelling. In: Proceedings of the 10th International Conference on Interaction Design and Children (2011)

  • Chang, A.: TinkRBooks: tinkerable story elements for emergent literacy. PhD thesis, Massachusetts Institute of Technology (2011)

  • Dale, Philip S., Fenson, Larry: Lexical development norms for young children. Behav. Res. Methods, Instrum. Comput. 28(1), 125–127 (1996)

    Article  Google Scholar 

  • Deerwester, Scott C, Dumais, Susan T, Landauer, Thomas K, Furnas, George W, Harshman, Richard A: Indexing by latent semantic analysis. JASIS 41(6), 391–407 (1990)

    Article  Google Scholar 

  • Eisenstein, J., Barzilay, R.: Bayesian unsupervised topic segmentation (2008)

  • Fellbaum, Christiane: WordNet. Wiley, New York (1998)

    MATH  Google Scholar 

  • Feng, M., Heffernan, N., Koedinger, K.: Addressing the testing challenge with a web-based e-assessment system that tutors as it assesses. In: WWW ’06 Proceedings of the 15th international conference on World Wide Web, pp. 307–316 (2006)

  • Gerlitz, C., Helmond, A.: The like economy: social buttons and the data-intensive web. New Media & Society, page 1461444812472322 (2013)

  • Haruechaiyasak, C., Damrongrat, C.: Article recommendation based on a topic model for wikipedia selection for schools. In: Digital Libraries: Universal and Ubiquitous Access to Information, pp. 339–342. Springer, Berlin (2008)

  • Hausendorf, H., Quasthoff, U.: Patterns of adult–child interaction as a machanism of discourse acquisition. J. Pragmat. 17, 241–259 (1992)

    Article  Google Scholar 

  • Havasi, C., Speer, R., Alonso, J.: ConceptNet: A Lexical Resource for Common Sense Knowledge, vol. 309, p. 269. John Benjamins Publishing Company, Amsterdam (2009)

    Google Scholar 

  • Hilbert, Dana D., Eis, Sarah D.: Early intervention for emergent literacy development in a collaborative community pre-kindergarten. Early Child. Educ. J. 42(2), 105–113 (2014)

    Article  Google Scholar 

  • Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57. ACM (1999)

  • Korat, O., Shamir, A.: The educational electronic book as a tool for supporting children’s emergent literacy in low versus middle ses groups. Comput. Educ. 50(1), 110–124 (2008)

    Article  Google Scholar 

  • Kouloumpis, Efthymios, Wilson, Theresa, Moore, Johanna D: Twitter sentiment analysis: The good the bad and the omg!. Icwsm 11, 538–541 (2011)

    Google Scholar 

  • Krause, A., Guestrin, C.: Data association for topic intensity tracking. Technical report, In: International Conference on Machine Learning (ICML) (2006)

  • Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 61–68. ACM (2009)

  • Lasecki, W.S., Miller, C.D., Bigham, J.P.: Warping time for more effective real-time crowdsourcing. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2033–2036. ACM (2013)

  • Levelt, Willem J.M.: Models of word production. Trends Cogn. Sci. 3(6), 223–232 (1999)

    Article  MathSciNet  Google Scholar 

  • Liangjie Hong and Brian D Davison. Empirical study of topic modeling in twitter. In: Proceedings of the First Workshop on Social Media Analytics, pp. 80–88. ACM (2010)

  • Linden, Greg, Smith, Brent, York, Jeremy: Amazon. com recommendations: Item-to-item collaborative filtering. Internet Comput. IEEE 7(1), 76–80 (2003)

    Article  Google Scholar 

  • Linell, P.: Approaching Dialogue, vol. 1. John Benjamins Publishing Company, Philadelphia (1998)

    Book  Google Scholar 

  • Loper, E., Bird, S.: Nltk : The natural language toolkit. Processing 1(July), 1–4 (2002)

    Google Scholar 

  • Mashhadi, A.J., Capra, L.: Quality control for real-time ubiquitous crowdsourcing. In: Proceedings of the 2nd international Workshop on Ubiquitous Crowdsouring, pp. 5–8. ACM (2011)

  • McCallum, A., Corrada-Emmanuel, A., Wang, X.: The author-recipient-topic model for topic and role discovery in social networks: Experiments with enron and academic email (2005)

  • McCallum, A.: Multi-label text classification with a mixture model trained by em. In: AAAI99 Workshop on Text Learning, pp. 1–7 (1999)

  • McFee, B., Bertin-Mahieux, T., Ellis, D.P.W., Lanckriet, G.R.G.: The million song dataset challenge. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 909–916. ACM (2012)

  • Miller, George A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  • Modi, J., Veloso, M., Smith, F.S., Oh, J.: Cmradar: a personal assistant agent for calendar management. In: Lecture Notes in Computer Science, vol. 3508, pp. 393 (2005)

  • Nuñez, D.S.: GlobalLit: a platform for collecting, analyzing, and reacting to children’s usage data on tablet computers. PhD thesis, Massachusetts Institute of Technology (2015)

  • Pak, Alexander, Paroubek, Patrick: Twitter as a corpus for sentiment analysis and opinion mining. LREc 10, 1320–1326 (2010)

    Google Scholar 

  • Pang, Bo, Lee, Lillian: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)

    Article  Google Scholar 

  • Parish-Morris, Julia, Mahajan, Neha, Hirsh-Pasek, Kathy, Golinkoff, Roberta Michnick, Collins, Molly Fuller: Once upon a time: parent–child dialogue and storybook reading in the electronic era. Mind Brain Educ. 7(3), 200–211 (2013)

    Article  Google Scholar 

  • Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems. The adaptive web, pp. 325–341. Springer, Berlin (2007)

    Google Scholar 

  • Perkins, J.: Python Text Processing with NLTK 2.0 Cookbook. Packt Publishing Ltd, Birmingham (2010)

    Google Scholar 

  • Pillinger, C., Wood, C.: Pilot study evaluating the impact of dialogic reading and shared reading at transition to primary school: early literacy skills and parental attitudes. Literacy 48, 155–163 (2014)

    Article  Google Scholar 

  • Plisson, J., Lavrac, N., Mladenic, D., et al.: A rule based approach to word lemmatization. In: Proceedings of IS-2004

  • Ricci, Francesco, Rokach, Lior, Shapira, Bracha: Introduction to Recommender Systems Handbook. Springer, Berlin (2011)

    Book  MATH  Google Scholar 

  • Rich, Charles, Sidner, Candace L: Collagen: A collaboration manager for software interface agents. User Model. User Adapt. Interact. 8, 315–350 (1998). doi:10.1023/A:1008204020038. ISSN 0924-1868

    Article  Google Scholar 

  • Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in e-commerce. In: Proceedings of the 1st ACM Conference on Electronic Commerce, pp. 158–166. ACM (1999)

  • Segal-Drori, O., Korat, O., Shamir, A., Klein, P.S.: Reading electronic and printed books with and without adult instruction: effects on emergent reading. Read. Writ. 23(8), 913–930 (2010)

    Article  Google Scholar 

  • Sleator, D.D.K., Temperley, D.: Parsing english with a link grammar. arXiv:cmp-lg/9508004 (preprint) (1995)

  • Sohn, Jongseo, Kim, Nam Soo, Sung, Wonyong: A statistical model-based voice activity detection. Signal Process. Lett. IEEE 6(1), 1–3 (1999)

    Article  Google Scholar 

  • Sommer, S., Schieber, A., Hilbert, A., Heinrich, K.: Analyzing customer sentiments in microblogs–a topic-model-based approach for twitter datasets. In: Proceedings of the Americas Conference on Information Systems (AMCIS) (2011)

  • Speer, R., Arnold, K., Havasi, C.: Divisi: Learning from semantic networks and sparse svd. In: Proceedings of 9th Python in Science Conference (SCIPY 2010) (2010)

  • Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: In KDD Workshop on Text Mining (2000)

  • van Duursma, E., Augustyn, Marilyn, Zuckerman, Barry: Reading aloud to children: the evidence. Arch. Dis. Child. 93(7), 554–557 (2008)

    Article  Google Scholar 

  • Wallach, H.M.: Topic modeling: beyond bag-of-words. In: Proceedings of the 23rd International Conference on Machine Learning, ICML ’06, pp. 977–984, New York, NY, USA, 2006. ACM. ISBN 1-59593-383-2. doi:10.1145/1143844.1143967

  • Wang, X., McCallum, A.: Topics over time: a non-markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 424–433. ACM (2006)

  • Weld, D., Adar, E., Chilton, L., Hoffmann, R., Horvitz, E., Koch, M., Landay, J., Lin, C., Mausam, M.: Personalized online education a crowdsourcing challenge. In: Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)

  • Wells, G.: Language development in the pre-school years, vol. 2. CUP Archive (1985)

  • Wenger, E.: Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge. Morgan Kaufmann, Burlington (2014)

    Google Scholar 

  • Whitehurst, Graver J., Lonigan, Christopher J.: Child development and emergent literacy. Child Dev 69(3), 848–872 (1998)

    Article  Google Scholar 

  • Wilbur, John W., Sirotkin, Karl: The automatic identification of stop words. J. Inf. Sci. 18(1), 45–55 (1992)

    Article  Google Scholar 

  • Zhao, W.X., Jiang, J., Weng, J., He, J., Lim, E.P., Yan, H., Li, X.: Comparing twitter and traditional media using topic models. In: Advances in Information Retrieval, pp. 338–349. Springer, Berlin (2011)

Download references

Acknowledgments

This work was supported by National Science Foundation Award Number 1117584.

Author information

Authors and Affiliations

  1. Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, USA

    Adrian Boteanu & Sonia Chernova

  2. Media Lab, Massachusetts Institute of Technology, Cambridge, USA

    David Nunez & Cynthia Breazeal

Authors
  1. Adrian Boteanu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sonia Chernova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Nunez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cynthia Breazeal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adrian Boteanu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Boteanu, A., Chernova, S., Nunez, D. et al. Fostering parent–child dialog through automated discussion suggestions. User Model User-Adap Inter 26, 393–423 (2016). https://doi.org/10.1007/s11257-016-9176-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11257-016-9176-8

Keywords

Search

Navigation