Educational Technology Research and Development

, Volume 67, Issue 1, pp 85–103 | Cite as

Applications of Pathfinder Network scaling for identifying an optimal use of first language for second language science reading comprehension

  • Kyung KimEmail author
  • Roy B. Clariana
Research Article


Previous research has shown that the knowledge structure (KS) complexity of a first language (L1) under certain conditions can strongly influence the KS complexity established in a second language (L2), and then this more complex L2 KS reciprocally influences L2 text comprehension. This present experimental investigation seeks to identify the unique contributions of mapping and writing in L1 (Korean) tasks to support L2 (English) science text comprehension using Pathfinder Network scaling, a graph-theoretic cognitive science approach. Native Korean low proficiency English language learners (n = 245) read a 708-word English (L2) science lesson text, completed one of seven treatment conditions, and then completed a comprehension posttest. The seven conditions consisted of three experimental conditions that required different L1 tasks including: L1 mapping alone, L1 writing alone, or both L1 mapping and writing; and four control conditions that did not receive any L1 treatment: L2 mapping alone, L2 writing alone, both L2 mapping and writing, or reading only. All of the maps and writing artifacts were converted into Pathfinder Networks that were compared to an expert’s referent network. Results show that requiring L1 lesson tasks relatively increases L2 KS complexity and concomitant comprehension posttest performance. In order of effectiveness, combined L1 mapping and writing was most effective for posttest comprehension, then L1 writing, and least effective is L1 mapping alone. These findings confirm and extend the earlier findings that the inherent L1 KS complexity can strongly influence L2 KS complexity. Educationally, requiring L1 tasks, especially in text translation, likely engenders richer L2 structure that supports higher-order understanding of the text. Also, these findings further validate this technology-based approach for measuring KS contained in bilingual learners’ productions.


Pathfinder Network Knowledge structure Concept map and writing Second language reading 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Anton, M., & DeCamilla, F. (1998). Socio-cognitive functions of L1 collaborative interaction in the L2 classroom. Canadian Modern Language Review, 54, 314–342. Scholar
  2. Barb, A. S., Clariana, R. B., & Chi-Ren, S. (2013). Applications of PathFinder Network Scaling for improving the ranking of satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3), 1092–1099.CrossRefGoogle Scholar
  3. Brooks, F. B., & Donato, R. (1994). Vygotskyan approaches to understanding foreign language learner discourse during communicative tasks. Hispania, 77, 262–274. Scholar
  4. Brysbaert, M., & Duyck, W. (2010). Is it time to leave behind the Revised Hierarchical Model of bilingual language processing after fifteen years of service? Bilingualism: Language and Cognition, 13, 359–371.CrossRefGoogle Scholar
  5. Chee, M. W. L., Hon, N., Lee, H. L., & Soon, C. S. (2001). Relative language proficiency modulates BOLD signal change when bilinguals perform semantic judgments. NeuroImage, 13, 1155–1163. Scholar
  6. Clariana, R. B., Engelmann, T., & Yu, W. (2013). Using centrality of concept maps as a measure of problem space states in computer-supported collaborative problem solving. Educational Technology Research and Development, 61, 423–442.CrossRefGoogle Scholar
  7. Clariana, R. B., & Koul, R. (2004). A computer-based approach for translating text into concept maplike representations. In Proceedings of the first international conference on concept mapping (pp. 14–17).Google Scholar
  8. Clariana, R. B., & Koul, P. (2008). The effects of learner prior knowledge when creating concept maps from a text passage. International Journal of Instructional Media, 35, 229–236.Google Scholar
  9. Clariana, R. B., & Taricani, E. M. (2010). The consequences of increasing the number of terms used to score open-ended concept maps. International Journal of Instructional Media, 37, 163–173.Google Scholar
  10. Clariana, R. B., Wallace, P. E., & Godshalk, V. M. (2009). Deriving and measuring group knowledge structure from essays: The effects of anaphoric reference. Educational Technology Research and Development, 57, 725–737.CrossRefGoogle Scholar
  11. Clariana, R. B., Wolfe, M. B., & Kim, K. (2014). The influence of narrative and expository text lesson text structures on knowledge structures: Alternate measures of knowledge structure. Educational Technology Research and Development, 62, 601–616.CrossRefGoogle Scholar
  12. Cohen, A. D., & Brooks-Carson, A. (2001). Research on direct versus translated writing: Students’ strategies and their results. The Modern Language Journal, 85, 169–188. Scholar
  13. Coronges, K. A., Stacy, A. W., & Valente, T. W. (2007). Structural comparison of cognitive associative networks in two populations. Journal of Applied Social Psychology, 37(9), 2097–2129.CrossRefGoogle Scholar
  14. Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281. Scholar
  15. Cumming, A. (1990). Metalinguistic and ideational thinking in second language composing. Written Communication, 7, 482–511. Scholar
  16. Draper, D. C. (2013). The instructional effects of knowledge-based community of practice learning environment on student achievement and knowledge convergence. Performance Improvement Quarterly, 25(4), 67–89.CrossRefGoogle Scholar
  17. Engelmann, T., & Hesse, F. W. (2010). How digital concept maps about the collaborators’ knowledge and information influence computer-supported collaborative problem solving. International Journal of Computer-Supported Collaborative Learning, 5(3), 299–319.CrossRefGoogle Scholar
  18. Fesel, S. S., Segers, E., Clariana, R. B., & Verhoeven, L. (2015). Quality of children’s knowledge representations in digital text comprehension: Evidence from pathfinder networks. Computers in Human Behavior, 48, 135–146. Scholar
  19. Gonzalvo, P., Canas, J. J., & Bajo, M. (1994). Structural representations in knowledge acquisition. Journal of Educational Psychology, 86, 601–616. Scholar
  20. Ifenthaler, D. (2010). Relational, structural, and semantic analysis of graphical representations and concept maps. Educational Technology Research and Development, 58, 81–97. Scholar
  21. Jamieson, J., Enright, M., & Chapelle, C. (2008). Building a validity argument for the Test of English as a Foreign Language. New York: Routledge.Google Scholar
  22. Jones, S., & Tetroe, J. (1987). Composing in a second language. In A. Matsuhashi (Ed.), Writing in real time: Modelling production processes (pp. 34–57). Norwood, NJ: Ablex.Google Scholar
  23. Karim, K. (2010). First language (L1) influence on second language (L2) reading: The role of transfer. Working Papers of the Linguistics Circle, 17, 49–54.Google Scholar
  24. Kim, M. K. (2012). Cross-validation study of methods and technologies to assess mental models in a complex problem solving situation. Computers in Human Behavior, 28(2), 703–717.CrossRefGoogle Scholar
  25. Kim, K. (2017a). Visualizing first and second language interactions in science reading: A knowledge structure network approach. Language Assessment Quarterly, 14, 328–345.CrossRefGoogle Scholar
  26. Kim, K. (2017b). Graphical interface of knowledge structure: A web-based research tool for representing knowledge structure in text. Technology, Knowledge and Learning. Scholar
  27. Kim, K. (2017c). An automatic measure of cross-language text structures. Technology, Knowledge and Learning. Scholar
  28. Kim, K., & Clariana, R. B. (2015). Knowledge structure measures of reader’s situation models across languages: Translation engenders richer structure. Technology, Knowledge and Learning, 20, 249–268.CrossRefGoogle Scholar
  29. Kim, K., & Clariana, R. B. (2016). Text signals influence second language expository text comprehension: Knowledge structure analysis. Educational Technology Research and Development. Scholar
  30. Kim, K., Clariana, R. B., Alqahtani, M. A., & Tang, H. (2016). Revealing knowledge structure in lesson texts using a computational text pattern-matching approach. Paper presented at the annual meeting of the Association for Educational Communication and Technology (AECT), Jacksonville, FL.Google Scholar
  31. Kobayashi, H., & Rinnert, C. (1992). Effects of first language on second language writing: Translation versus direct composition. Language Learning, 42, 183–215. Scholar
  32. Li, P., & Clariana, R. B. (2018). Reading comprehension in L1 and L2: An integrative approach. Journal of Neurolinguistics.Google Scholar
  33. Li, P., Zhang, F., Tsai, E., & Puls, B. (2014). Language history questionnaire (LHQ 2.0): A new dynamic web-based research tool. Bilingualism: Language and Cognition, 17(03), 673–680. Scholar
  34. Pennington, M., & So, S. (1993). Comparing writing process and product across two languages: A study of six Singaporean university student writers. Journal of Second Language Writing, 2, 41–63. Scholar
  35. Poindexter, M. T., & Clariana, R. B. (2006). The influence of relational and proposition-specific processing on structural knowledge and traditional learning outcomes. International Journal of Instructional Media, 33, 177–184.Google Scholar
  36. Qi, D. S. (1998). An inquiry into language-switching in second language composing processes. The Canadian Modern Language Review, 54, 413–435. Scholar
  37. Reyna, V. F., & Brainerd, C. J. (1995). Fuzzy-trace theory: An interim synthesis. Learning and Individual Differences, 7, 1–75.CrossRefGoogle Scholar
  38. Roca de Larios, J., Murphy, L., & Manchon, R. (1999). The use of restructuring strategies in EFL writing: A study of Spanish learners of English as a Foreign Language. Journal of Second Language Writing, 8, 13–44. Scholar
  39. Ryan, C. (2013). American Community Survey Reports: Language Use in the United States: 2011.
  40. Sarwar, G. S. (2011). Structural assessment of knowledge for misconceptions: Effectiveness of structural feedback provided by pathfinder networks in the domain of physics. Kolln: LAP Lambert Academic Publishing.Google Scholar
  41. Schmidt, S. (2009). Shall we really do it again? The powerful concept of replication is neglected in the social sciences. Review of General Pscyhology, 13, 90–100. Scholar
  42. Spector, J. M., Johnson, T. E., & Young, P. A. (2015). An editorial on replication studies and scaling up efforts. Educational Technology Research and Development, 63, 1–4. Scholar
  43. Stahl, G. (2010). Group cognition as a foundation for the new science of learning. In M. S. Khine & I. M. Saleh (Eds.), New science of learning: Cognition, computers, and collaboration in education (pp. 23–34). New York, NK: Springer.CrossRefGoogle Scholar
  44. Tang, H., & Clariana, R. (2017). Leveraging a sorting task as a measure of knowledge structure in bilingual settings. Technology, Knowledge and Learning, 22(1), 23–35.CrossRefGoogle Scholar
  45. Taricani, E. M., & Clariana, R. B. (2006). A technique for automatically scoring open-ended concept maps. Educational Technology Research and Development, 54, 61–78.CrossRefGoogle Scholar
  46. Tawfik, A. A., Law, V., Ge, X., Xing, W., & Kim, K. (2018). The effect of sustained vs. faded scaffolding on students’ argumentation in ill-structured problem solving. Computers in Human Behavior.Google Scholar
  47. Tossell, C. C., Schvaneveldt, R. W., & Branaghan, R. J. (2010). Targeting knowledge structures: A new method to elicit the relatedness of concepts. Cognitive Technology, 15(2), 11.Google Scholar
  48. Tse, C. S., & Altarriba, J. (2012). The effects of first-and second-language proficiency on conflict resolution and goal maintenance in bilinguals: Evidence from reaction time distributional analyses in a Stroop task. Bilingualism: Language and Cognition, 15(03), 663–676.CrossRefGoogle Scholar
  49. Uzawa, K., & Cumming, A. (1989). Writing strategies in Japanese as a foreign language: lowering or keeping up the standards. The Canadian Modern Language Review, 46, 178–194.CrossRefGoogle Scholar
  50. van Hell, J. G., & Kroll, J. F. (2013). Using electrophysiological measures to track the mapping of words to concepts in the bilingual brain: a focus on translation. Cambridge: Cambridge University Press. Retrieved from
  51. Wang, L. (2003). Switching to first language among writers with differing second-language proficiency. Journal of Second Language Writing, 12, 347–375. Scholar
  52. Wang, W., & Wen, Q. (2002). L1 use in the L2 composing process: An exploratory study of 16 Chinese EFL writers. Journal of Second Language Writing, 11, 225–246. Scholar
  53. Woodall, B. R. (2002). Language-switching: Using the first language while writing in a second. Journal of Second Language Writing, 11, 7–28. Scholar
  54. Zhao, X., & Li, P. (2013). Simulating cross-language priming with a dynamic computational model of the lexicon. Bilingualism: Language and Cognition, 16, 288–303.CrossRefGoogle Scholar
  55. Zimmerman, W. A., Kang, H. B., Kim, K., Gao, M., Johnson, G., Clariana, R., et al. (2018). Computer-automated approach for scoring short essays in an introductory statistics course. Journal of Statistics Education, 26(1), 40–47.CrossRefGoogle Scholar

Copyright information

© Association for Educational Communications and Technology 2018

Authors and Affiliations

  1. 1.Educational Technology Research and AssessmentNorthern Illinois UniversityDekalbUSA
  2. 2.Learning, Design, and TechnologyThe Pennsylvania State UniversityUniversity ParkUSA

Personalised recommendations