Future directions for assessing for learning in second language writing research: epilogue to the special issue

References

• Alderson, J. C., Haapakangas, E.-L., Huhta, A., Nieminen, L., & Ullakonoja, R. (2015). The diagnosis of reading in a second or foreign language. New York, NY: Routledge.
   Google Scholar
• Anderson, L. W. (2002). Curricular alignment: A re-examination. Theory into Practice, 41, 255–260.10.1207/s15430421tip4104_9
   Web of Science ®Google Scholar
• Aryadoust, V. (2011). Application of the fusion model to while-listening performance tests. SHIKEN: JALT TESTING & Evaluation SIG Newsletter, 15, 2–9.
   Google Scholar
• Aryadoust, V. (2015). Self- and peer assessments of oral presentations by first-year university students. Educational Assessment, 20, 199–225.10.1080/10627197.2015.1061989
   Web of Science ®Google Scholar
• Bai, L., & Hu, G. (2017). In the face of fallible AWE feedback: How do students respond? Educational Psychology, 37(1). doi:10.1080/01443410.2016.1223275
   Google Scholar
• Biggs, J. (2003). Teaching for quality learning at university: What the student does (2nd ed.). Buckingham: Open University Press.
   Google Scholar
• Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5, 7–74.10.1080/0969595980050102
   Google Scholar
• Borsboom, D. (2006). The attack of the psychometricians. Psychometrika, 71, 425–440.
   PubMed Web of Science ®Google Scholar
• Buckle, C. F., & Riding, R. J. (1988). Current problems in assessment – Some reflections. Educational Psychology, 8, 299–306.10.1080/0144341880080410
   Google Scholar
• Cattell, R. B., Cattell, A. K. S., & Rhymer, R. M. (1947). P-technique demonstrated in determining psychophysiological source traits in a normal individual. Psychometrika, 12, 267–288.10.1007/BF02288941
   PubMed Web of Science ®Google Scholar
• Chai, C. S., Koh, J. H. L., & Tsai, C. C. (2010). Facilitating preservice teachers’ development of technological, pedagogical, and content knowledge (TPACK). Educational Technology and Society, 13, 63–73.
   Web of Science ®Google Scholar
• Chapelle, C. A., Cotos, E., & Lee, J. (2015). Validity arguments for diagnostic assessment using automated writing evaluation. Language Testing, 32, 385–405.10.1177/0265532214565386
   Web of Science ®Google Scholar
• Chen, C. F. E., & Cheng, W. Y. E. (2008). Beyond the design of automated writing evaluation: Pedagogical practices and perceived learning effectiveness in EFL writing classes. Language Learning & Technology, 12, 94–112.
   Web of Science ®Google Scholar
• Cheung, Y. L. (2015). Feedback from automated essay evaluation systems: A review of selected research. TESL Reporter, 48(2), 1–15.
   Google Scholar
• Clauser, B. E., Kane, M. T., & Swanson, D. B. (2002). Validity issues for performance-based tests scored with computer-automated scoring systems. Applied Measurement in Education, 15, 413–432.10.1207/S15324818AME1504_05
   Web of Science ®Google Scholar
• Condon, W. (2013). Large-scale assessment, locally-developed measures, and automated scoring of essays: Fishing for red herrings? Assessing Writing, 18, 100–108.10.1016/j.asw.2012.11.001
   Web of Science ®Google Scholar
• Conference on College Composition and Communication. (2004). Position statement on teaching, learning, and assessing writing in digital environment. Retrieved from http://www.ncte.org/cccc/resources/positions/digitalenvironments
   Google Scholar
• Conference on College Composition and Communication. (2009). Writing assessment: A position statement. Retrieved from http://www.ncte.org/cccc/resources/positions/writingassessment
   Google Scholar
• DiBello, L. V., Roussos, L. A., & Stout, W. F. (2007). Review of cognitively diagnostic assessment and a summary of psychometric models. In C. R. Rao & S. Sinharay (Eds.), Handbook of statistics, vol. 26: Psychometrics (pp. 979–1030). Amsterdam: Elsevier.
   Google Scholar
• Dikli, S. (2010). The nature of automated essay scoring feedback. Computer Assisted Language Instruction Consortium, 28, 99–134.
   Google Scholar
• Dikli, S., & Bleyle, S. (2014). Automated essay scoring feedback for second language writers: How does it compare to instructor feedback? Assessing Writing, 22, 1–17.10.1016/j.asw.2014.03.006
   Web of Science ®Google Scholar
• Ellis, J. L., & van den Wollenberg, A. L. (1993). Local homogeneity in latent trait models. A characterization of the homogeneous monotone IRT model. Psychometrika, 58, 417–429.10.1007/BF02294649
   Web of Science ®Google Scholar
• Fu, S. J. (2013). ICT in education: A critical literature review and its implications. International Journal of Education and Development using Information and Communication Technology, 9, 112–125.
   Google Scholar
• Grimes, D., & Warschauer, M. (2008). Learning with laptops: A multi-method case study. Journal of Educational Computing Research, 38, 305–332.10.2190/EC.38.3.d
   Web of Science ®Google Scholar
• Grimes, D., & Warschauer, M. (2010). Utility in a fallible tool: A multi-site case study of automated writing evaluation. Journal of Technology, Learning, and Assessment, 8(6), 1–43.
   Google Scholar
• Harlen, W. (2005). Teachers’ summative practices and assessment for learning – Tensions and synergies. Curriculum Journal, 16, 207–223.10.1080/09585170500136093
   Google Scholar
• Jang, E. E., Dunlop, M., Park, G., & van der Boom, E. H. (2015). How do young students with different profiles of reading skill mastery, perceived ability, and goal orientation respond to holistic diagnostic feedback? Language Testing, 32, 359–383.10.1177/0265532215570924
   Web of Science ®Google Scholar
• Johns, A. M. (2008). Genre awareness for the novice academic student: An on-going quest. Language Teaching, 41, 237–252.
   Google Scholar
• Kim, Y. H. (2011). Diagnosing EAP writing ability using the reduced reparameterized unified model. Language Testing, 28, 509–541.10.1177/0265532211400860
   Web of Science ®Google Scholar
• King, R. B., & McInerney, D. M. (2014). Mapping changes in students’ English and math self-concepts: A latent growth model study. Educational Psychology, 34, 581–597.10.1080/01443410.2014.909009
   Web of Science ®Google Scholar
• Koc, M. (2005). Implications of learning theories for effective technology integration and preservice teacher training: A critical literature review. Journal of Turkish Science Education, 2, 2–18.
   Google Scholar
• Lee, I., & Coniam, D. (2013). Introducing assessment for learning for EFL writing in an assessment of learning examination-driven system in Hong Kong. Journal of Second Language Writing, 22, 34–50.
   Web of Science ®Google Scholar
• Liu, Q., & Brown, D. (2015). Methodological synthesis of research on the effectiveness of corrective feedback in L2 writing. Journal of Second Language Writing, 30, 66–81.10.1016/j.jslw.2015.08.011
   Web of Science ®Google Scholar
• Liu, S., & Kunnan, A. J. (2016). Investigating the application of automated writing evaluation to Chinese undergraduate English majors: A case study of WriteToLearn. Computer Assisted Language Instruction Consortium, 33, 71–91.
   Google Scholar
• Molenaar, P., & Campbell, C. (2009). The new person-specific paradigm in psychology. Current Directions in Psychological Science, 18, 112–117.10.1111/cdir.2009.18.issue-2
   Web of Science ®Google Scholar
• Molenaar, P. C. M., de Gooijer, J. G., & Schmitz, B. (1992). Dynamic factor analysis of nonstationary multivariate time series. Psychometrika, 57, 333–349.10.1007/BF02295422
   Web of Science ®Google Scholar
• Powers, D. E., Escoffery, D. S., & Duchnowski, M. P. (2015). Validating automated essay scoring: A (modest) refinement of the “gold standard”. Applied Measurement in Education, 28, 130–142.10.1080/08957347.2014.1002920
   Web of Science ®Google Scholar
• Rakedzon, T., & Baram-Tsabari, A. (2017). Assessing and improving L2 graduate students’ popular science and academic writing in an academic writing course. Educational Psychology, 37(1). doi:10.1080/01443410.2016.1192108
   Google Scholar
• Ram, N., Brose, A., & Molenaar, P. C. M. (2013). Dynamic factor analysis: Modeling person-specific process. In T. D. Little (Ed.), The Oxford handbook of quantitative methods: Vol. 2, statistical analysis (pp. 441–457). New York, NY: Oxford University Press.
   Google Scholar
• Ramineni, C. (2013). Validating automated essay scoring for online writing placement. Assessing Writing, 18, 40–61.10.1016/j.asw.2012.10.005
   Web of Science ®Google Scholar
• Ranalli, J., Link, S., & Chukharev-Hudilainen, E. (2017). Automated writing evaluation for formative assessment of second language writing: Investigating the accuracy and usefulness of feedback as part of argument-based validation. Educational Psychology, 37(1). doi:10.1080/01443410.2015.1136407
   Google Scholar
• Riazi, A. M., & Rezaii, M. (2011). Teacher- and peer-scaffolding behaviors: Effects on EFL students’ writing improvement (pp. 55–62). Paper presented at the Proceedings of CLESOL 2010 Conference, 12th National Conference for Community Languages & ESOL (CLESOL), Dunedin, New Zealand.
   Google Scholar
• Shermis, M. D. (2014). State-of-the-art automated essay scoring: Competition, results, and future directions from a United States demonstration. Assessing Writing, 20, 53–76.10.1016/j.asw.2013.04.001
   Web of Science ®Google Scholar
• Shermis, M. D., Burstein, J. C., & Bursky, S. (2013). Introduction to automated essay evaluation. In M. D. Shermis & J. C. Burstein (Eds.), Handbook of automated essay evaluation: Current applications and new directions (pp. 1–15). New York, NY: Routledge.
   Google Scholar
• Shermis, M. D., & Burstein, J. (Eds.). (2013). Handbook of automated essay evaluation: Current applications and new directions. New York, NY: Routledge.
   Google Scholar
• Stevenson, M., & Phakiti, A. (2014). The effects of computer generated feedback on the quality of writing. Assessing Writing, 19, 51–65.
   Web of Science ®Google Scholar
• Tatsuoka, K. K. (1983). Rule-space: An approach for dealing with misconceptions based on item response theory. Journal of Educational Measurement, 20, 34–38.
   Web of Science ®Google Scholar
• Tiekstra, M., Minnaert, A., & Hessels, M. G. P. (2016). A review scrutinising the consequential validity of dynamic assessment. Educational Psychology, 36, 112–137.10.1080/01443410.2014.915930
   Web of Science ®Google Scholar
• Tyler, R. (1949). Basic principles of curriculum and instruction. Chicago, IL: University of Chicago Press.
   Google Scholar
• Wang, Z. (2015). Examining big-fish-little-pond-effects across 49 countries: A multilevel latent variable modelling approach. Educational Psychology, 35, 228–251.10.1080/01443410.2013.827155
   Web of Science ®Google Scholar
• Weigle, S. C. (2013). English language learners and automated scoring of essays: Critical considerations. Assessing Writing, 18, 85–99.
   Web of Science ®Google Scholar
• Wu, S. M., & Tan, S. (2016). Rubric design for a multimodal presentation task: A case study of an environmental science module in Singapore. In V. Aryadoust & J. Fox (Eds.), Trends in language assessment research and practice: The view from the Middle East and the Pacific Rim (pp. 334–360). Newcastle: Cambridge Scholars Publishing.
   Google Scholar
• Xie, Q. (2017). Diagnosing university students’ academic writing in English: Is cognitive diagnostic modelling the way forward? Educational Psychology, 37(1). doi:10.1080/01443410.2016.1202900
   Google Scholar
• Yang, Y., Buckendahl, C. W., Juszkiewicz, P. J., & Bhola, D. S. (2002). A review of strategies for validating computer-automated scoring. Applied Measurement in Education, 15, 391–412.10.1207/S15324818AME1504_04
   Web of Science ®Google Scholar
• You, S., & Sharkey, J. (2009). Testing a developmental – Ecological model of student engagement: A multilevel latent growth curve analysis. Educational Psychology, 29, 659–684.10.1080/01443410903206815
   Web of Science ®Google Scholar