Homework and achievement: Using smartpen technology to find the connection

Journal of Educational Psychology

Volumn 109, Issue 2, 2017, Pages 208-219

  Rawson, Kevin ^a   Stahovich, Thomas F ^a   Mayer, Richard E ^b  

^a University of California Riverside   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Data mining; Educational technology; Homework; Time on task

Indexed keywords

EID: 84971472972     PISSN: 00220663     EISSN: 19392176     Source Type: Journal    
DOI: 10.1037/edu0000130     Document Type: Article

References (56)

1. Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Erlbaum.
2. Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. Journal of the Learning Sciences, 4, 167-207. http://dx.doi.org/10.1207/s15327809jls0402_2
3. Anthony, L., Yang, J., & Koedinger, K. R. (2008). Toward next-generation intelligent tutors: Adding natural handwriting input. IEEE MultiMedia, 15, 64-68. http://dx.doi.org/10.1109/MMUL.2008.73
4. Antonenko, P. D., Toy, S., & Niederhauser, D. S. (2012). Using cluster analysis for data mining in educational technology research. Educational Technology Research and Development, 60, 383-398. http://dx.doi.org/ 10.1007/s11423-012-9235-8
5. Atkinson, R. C. (1968). Computerized instruction and the learning process. American Psychologist, 23, 225-239. http://dx.doi.org/10.1037/ h0020791
6. Beal, C. R., & Cohen, P. R. (2008). Temporal data mining for educational applications. Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence, 5351, 66-77.
7. Berliner, D. (1984). The half-full glass: A review of research on teaching. In P. L. Hosford (Ed.), Using what we know about teaching (pp. 51-84). Alexandria, VA: Association for Supervision and Curriculum Development.
8. Berliner, D. (1991). What's all this fuss about instructional time? In M. Ben-Peretz & R. Bromme (Eds.), The nature of time in schools. New York, NY: Teachers College Press.
9. Blanc, P. (1999). Solving a non-routine problem: What helps, what hinders. Proceedings of the British Society for Research into Learning Mathematics, 19, 1-6.
10. Blumner, H. N., & Richards, H. C. (1997). Study habits and academic achievement of engineering students. The Journal of Engineering Education, 86, 125-132. http://dx.doi.org/10.1002/j.2168-9830.1997.tb00275.x
11. Carroll, J. B. (1963). A model of school learning. Teachers College Record, 64, 723-733.
12. Clark, R. C., & Mayer, R. E. (2011). e-Learning and the science of instruction (3rd ed.). San Francisco, CA: Pfeiffer.
13. Cooper, H. (1989). Homework. White Plains, NY: Longman. http://dx.doi.org/10.1037/11578-000
14. Cooper, H. (2001). The battle over homework (2nd ed.). Thousand Oaks, CA: Corwin.
15. Cooper, H., Robinson, J. C., & Patall, E. A. (2006). Does homework improve academic achievement? A synthesis of research 1987-2003. Review of Educational Research, 76, 1-62. http://dx.doi.org/10.3102/ 00346543076001001
16. Credé, M., & Kuncel, N. R. (2008). Study habits, skills, and attitudes: The third pillar supporting collegiate academic performance. Perspectives on Psychological Science, 3, 425-453. http://dx.doi.org/10.1111/j.1745-6924.2008.00089.x
17. Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students' learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14, 4-58. http://dx.doi.org/10.1177/1529100612453266.
18. Ebbinghaus, H. (1964). Memory. New York, NY: Dover. (Original work published 1885)
19. Graham, S., & Williams, C. (2009). An attributional approach to motivation in school. In K. R. Wentzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 11-34). New York, NY: Routledge.
20. Hall, R. (2000). Video recording as theory. In D. Lesh & A. Kelley (Eds.), Handbook of research design in mathematics and science education (pp. 647-664). Mahwah, NJ: Lawrence Erlbaum.
21. Hattie, J. A. C. (2009). Visible learning: A synthesis of over 800 metaanalyses relating to achievement. New York, NY: Routledge.
22. Herold, J., & Stahovich, T. (2012). Characterizing students handwritten self-explanations. Proceedings of the American Society for Engineering Education 119th Annual Conference and Exposition, San Antonio, TX.
23. Herold, J., Stahovich, T., Lin, H.-L., & Calfee, R. C. (2011, June). The effectiveness of "pencasts" as an instructional medium. Proceedings of the American Society for Engineering Education 118th Annual Conference and Exposition, Vancouver, British Columbia, Canada.
24. Herold, J., Stahovich, T. F., & Rawson, K. (2013). Using educational data mining to identify correlations between homework effort and performance. Proceedings of the American Society for Engineering Education 120th Annual Conference and Exposition, Atlanta, GA.
25. Jones, L., & Ruch, G. M. (1928). The twenty-seventh yearbook of the national society for the study of education: Nature and nurture, part ii-their influence upon achievement. In G. M. Whipple (Ed.), Achievement as affected by amount of time spent in study (pp. 131-134). Bloomington, IL: Public School Publishing Co.
26. Karweit, N. (1984). Timed-on-task reconsidered: Synthesis of research on time and learning. Educational Leadership, 41, 32-35.
27. Keith, T. Z. (1982). Time spent on homework and high school grades: A large-scale path analysis. Journal of Educational Psychology, 74, 248-253. http://dx.doi.org/10.1037/0022-0663.74.2.248
28. Koedinger, K. R., D'Mello, S., McLaughlin, E. A., Pardos, Z. A., & Rosé, C. P. (2015). Data mining and education. Wiley Interdisciplinary Reviews: Cognitive Science, 6, 333-353. http://dx.doi.org/10.1002/wcs.1350
29. Krüger, A., Merceron, A., & Wolf, B. (2010). A data model to ease analysis and mining of educational data. In R. S. J. d. Baker, A. Merceron, & P. I. Pavlik (Eds.), Proceedings of the 3rd International Conference on Educational Data Mining (pp. 131-140). Pittsburgh, PA.
30. Lee, C.-K., Stahovich, T., & Calfee, R. C. (2011). A pen-based statics tutoring system. Proceedings of the American Society for Engineering Education 118th Annual Conference and Exposition, Vancouver, British Columbia, Canada.
31. Li, N., Cohen, W. W., Koedinger, K. R., & Matsuda, N. (2011). A machine learning approach for automatic student model discovery. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, & J. Stamper (Eds.), Proceedings of the 4th International Conference on Educational Data Mining (pp. 31-40). Eindhoven, the Netherlands.
32. Lin, H., Stahovich, T. F., & Herold, J. (2012). Automatic handwritten statics solution classification and its applications in predicting student performance. Proceedings of the 119th American Society for Engineering Education Annual Conference and Exposition, San Antonio, TX.
33. Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an "early warning system" for educators: A proof of concept. Computers & Education, 54, 588-599. http://dx.doi.org/10.1016/j.compedu.2009.09.008
34. Mayer, R. E. (2009). Multimedia learning (2nd ed.). New York, NY: Cambridge University Press. http://dx.doi.org/10.1017/CBO97805 11811678
35. Mayer, R. E. (2011). Applying the science of learning. Upper Saddle River, NJ: Pearson.
36. Mayer, R. E. (2014). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 43-71). New York, NY: Cambridge University Press. http://dx.doi.org/10.1017/CBO9781139547369.005
37. Mostow, J., González-Brenes, J. P., & Tan, B. H. L. (2011). Learning classifiers from a relational database of tutor logs. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, & J. Stamper (Eds.), Proceedings of the 4th International Conference on Educational Data Mining (pp. 149-158). Eindhoven, Netherlands.
38. Mueller, P. A., & Oppenheimer, D. M. (2014). The pen is mightier than the keyboard: Advantages of longhand over laptop note taking. Psychological Science, 25, 1159-1168. http://dx.doi.org/10.1177/0956797 614524581
39. Oviatt, S., Arthur, A., & Cohen, J. (2006). Quiet interfaces that help students think. Proceedings of the 19th Annual ACM Symposium on User Interface Software and Technology- UIST '06 (pp. 191-200). New York, NY: ACM Press.
40. Rawson, K., & Stahovich, T. F. (2013, June). Predicting course performance from homework habits. Proceedings of the American Society for Engineering Education 120th Annual Conference and Exposition, Atlanta, GA.
41. Romero, C., Romero, J., Luna, J., & Ventura, S. (2010, June 11-13). Mining rare association rules from e-learning data. In R. S. J. d. Baker, A. Merceron, & P. I. Pavlik Jr. (Eds.), Proceedings of the 3rd International Conference on Educational Data Mining (pp. 171-180). Pittsburgh, PA.
42. Romero, C., Ventura, S., Vasilyeva, E., & Pechenizkiy, M. (2010). Class association rules mining from students' test data. In R. S. J. d. Baker, A. Merceron, & P. I. Pavlik Jr. (Eds.), Proceedings of the 3rd International Conference on Educational Data Mining (pp. 131-140). Pittsburgh, PA.
43. Schuman, H., Walsh, E., Olson, C., & Etheridge, B. (1985). Effort and reward: The assumption that college grades are affected by quantity of study. Social Forces, 63, 945-966. http://dx.doi.org/10.1093/sf/63.4.945
44. Schunk, D. H., & Pajares, F. (2009). Self-efficacy theory. In K. R. Wentzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 35-54). New York, NY: Routledge.
45. Shanabrook, D. H., Cooper, D. G., Woolf, B. P., & Arroyo, I. (2010). Identifying high-level student behavior using sequence-based motif discovery. In R. S. J. d. Baker, A. Merceron, & P. I. Pavlik Jr. (Eds.), Proceedings of the 3rd International Conference on Educational Data Mining (pp. 191-200). Pittsburgh, PA.
46. Shavelson, R. J., & Towne, L. (Eds.). (2002). Scientific research in education. Washington, DC: National Academy Press.
47. Steif, P. S., & Dantzler, J. A. (2005). A Statics Concept Inventory: Development and psychometric analysis. The Journal of Engineering Education, 94, 363-371. http://dx.doi.org/10.1002/j.2168-9830.2005.tb00864.x
48. Steif, P. S., & Dollár, A. (2009). Study of usage patterns and learning gains in a web-based interactive static course. The Journal of Engineering Education, 98, 321-333. http://dx.doi.org/10.1002/j.2168-9830.2009.tb01030.x
49. Steif, P. S., Lobue, J. M., Kara, L. B., & Fay, A. L. (2010). Improving problem solving performance by inducing talk about salient problem features. The Journal of Engineering Education, 99, 135-142. http://dx.doi.org/10.1002/j.2168-9830.2010.tb01050.x
50. Stevens, R., Johnson, D. F., & Soller, A. (2005). Probabilities and predictions: Modeling the development of scientific problem-solving skills. Cell Biology Education, 4, 42-57. http://dx.doi.org/10.1187/cbe.04-03-0036
51. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. New York, NY: Springer. http://dx.doi.org/10.1007/978-1-4419-8126-4
52. Trivedi, S., Pardos, Z. A., Sárközy, G. N., & Heffernan, N. T. (2011). Spectral clustering in educational data mining. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, & J. Stamper (Eds.), Proceedings of the 4th International Conference on Educational Data Mining (pp. 129-138). Eindhoven, Netherlands.
53. Van Arsdale, T., & Stahovich, T. (2012, June). Does neatness count? What the organization of student work says about understanding. Proceedings of the American Society for Engineering Education 119th Annual Conference and Exposition, San Antonio, TX.
54. van Gog, T. (2013). Time on task. In J. Hattie & E. M. Anderman (Eds.), International guide to student achievement (pp. 432-433). New York, NY: Routledge.
55. van Gog, T., Ericsson, K. A., Rikers, R. M. J. P., & Paas, F. (2005). Instructional design for advanced learners: Establishing connections between the theoretical frameworks of cognitive load and deliberate practice. Educational Technology Research and Development, 53, 73-81. http://dx.doi.org/10.1007/BF02504799
56. Xu, J. (2013). Homework and academic achievement. In J. Hattie & E. M. Anderman (Eds.), International guide to student achievement (pp. 199-201). New York, NY: Routledge.