Structure of the Observed Learning Outcomes (SOLO) model: A mixed-method systematic review of research in mathematics education

Saidat Morenike Adeniji; Penelope Baker; Martin Schmude

doi:10.29333/ejmste/12087

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Saidat Morenike Adeniji ¹ ² ^* , Penelope Baker ¹ , Martin Schmude ¹

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¹ University of New England, Armidale, AUSTRALIA² University of Ilorin, Ilorin, NIGERIA^* Corresponding Author

Abstract

The review followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) standard to search and report relevant articles on the use of SOLO model in mathematics. A systematic search was conducted in Education Source, ERIC, JSTOR, and PsycINFO databases and yielded 198 studies. After screening and appraisal, 62 papers (37 qualitative, 17 quantitative, and 8 mixed-method studies) published in English between 1990 and 2020 were reported using a narrative synthesis. The findings indicated that SOLO model appropriately reflects students' learning outcomes; there is a direct relationship between students’ performances and their SOLO levels; and SOLO model could explain several other developmental theories and contribute to the development of mathematics curricula. These findings highlight the gaps between theory and practice of this model in mathematics education, and informs education professionals about the diverse applications of the SOLO model for improving mathematics teaching and learning, fair assessment, and curriculum development.

Keywords

Mathematics assessment
mathematics teaching and learning
SOLO model
systematic review

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Review Article

EURASIA J Math Sci Tech Ed, 2022, Volume 18, Issue 6, Article No: em2119

https://doi.org/10.29333/ejmste/12087

Publication date: 14 May 2022

Article Views: 3114

Article Downloads: 5290

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Declaration of Conflict of Interest: No conflict of interest is declared by author(s).

Data sharing statement: Data supporting the findings and conclusions are available upon request from the corresponding author(s).

References

Afriyani, D., Sa’dijah, C., Subanji, & Muksar, M. (2018). Characteristics of students’ mathematical understanding in solving multiple representation task based on solo taxonomy. International Electronic Journal of Mathematics Education, 13(3), 281-287. https://doi.org/10.12973/iejme/3920
Alsaadi, A. (2001). A comparison of primary mathematics curriculum in England and Qatar: The SOLO taxonomy. Research into Learning Mathematics, 21(3), 1.
Aoyama, K., & Stephens, M. (2003). Graph interpretation aspects of statistical literacy: A Japanese perspective. Mathematics Education Research Journal, 15(3), 207-225. https://doi.org/10.1007/BF03217380
Apawu, J., Owusu-Ansah, N. A., & Akayuure, P. (2018). A study on the algebraic working processes of senior high school students in Ghana. European Journal of Science and Mathematics Education, 6(2), 62-68. https://doi.org/10.30935/scimath/9523
Assessment Reform Group. (2002). Assessment for learning: 10 principles. https://pdf4pro.com/cdn/assessment-for-learning-10-principles-hkeaa-2b14b0.pdf
Atasoy, E., & Konyalihatipoglu, M. E. (2019). Investigation of students’ holistic and analytical thinking styles in learning environments assisted with dynamic geometry software. Eğitim ve Bilim [Education and Science], 44(199), 49-74. http://doi.org/10.15390/eb.2019.8003
Bhagwat, M. S. (2017). Developing and implementing instructional strategy on the structure of observed learning outcomes (SOLO) taxonomy for mathematics of class-ix. [Unpublished doctoral thesis]. Maharaja Sayajirao University of Baroda, India.
Biggs, J. B., & Collis, K. F. (1982). Evaluating the quality of learning: The SOLO taxonomy (structure of the observed learning outcome). Academic Press.
Biggs, J. B., & Collis, K. F. (1991). Multimodal learning and the quality of intelligent behavior. Lawrence Erlbaum.
Brabrand, C., & Dahl, B. (2009). Using the SOLO taxonomy to analyze competence progression of university science curricula. Higher Education, 58(4), 531-549. https://doi.org/10.1007/s10734-009-9210-4
Callingham, R., & Pegg, J. (2010). Using developmental frameworks to support curriculum outcomes. In L. Sparrow, B. Kissane, & C. Hurst (Eds.), Shaping the future of mathematics education: Proceedings of the 33^rd Annual Conference of the Mathematics Education Research Group of Australasia (pp. 125-132). MERGA.
Campbell, K. A. (2006). The SOLO taxonomy and probability: Development of a set of superitems [Unpublished doctoral dissertation]. State University of New York, Albany.
Caniglia, J. C., & Meadows, M. (2018). An application of the SOLO taxonomy to classify strategies used by pre-service teachers to solve “one question problems”. Australian Journal of Teacher Education, 43(9), 75-89. https://doi.org/10.14221/ajte.2018v43n9.5
Cetin, B., & Ilhan, M. (2017). An analysis of rater severity and leniency in open-ended mathematic questions rated through standard rubrics and rubrics based on the SOLO taxonomy. Eğitim ve Bilim [Education and Science], 42, 189. https://doi.org/10.15390/EB.2017.5082
Chan, C. C., Tsui, M., Chan, M. Y., & Hong, J. H. (2002). Applying the structure of the observed learning outcomes (SOLO) taxonomy on student’s learning outcomes: An empirical study. Assessment & Evaluation in Higher Education, 27(6), 511-527. https://doi.org/10.1080/0260293022000020282
Chaphalkar, R. M. (2016). A longitudinal study of students’ reasoning about variation in distributions in an introductory college statistics course [Unpublished PhD thesis]. University of Montana.
Chick, H. (1998). Cognition in the formal modes: Research mathematics and the SOLO taxonomy. Mathematics Education Research Journal, 10(2), 4-26. https://doi.org/10.1007/BF03217340
Drefs, M. A. (2006). Assessment of number concepts in kindergarten and grade one students [Unpublished doctoral thesis]. University of Calgary, Calgary, AB. https://doi.org/10.11575/PRISM/1601
Easdown, D., Papadopoulos, G., & Zheng, C. (2019). Summer school versus term-time for fundamental mathematics at the tertiary level. International Journal of Innovation in Science and Mathematics Education, 27(5), 13-26. https://doi.org/10.30722/IJISME.27.05.002
Elassabi, A., & Kacar, A. (2020). Investigation of Libyan and Turkish students’ thinking levels in solving quadratic word problems based on SOLO taxonomy. PEGEM Journal of Education and Instruction, 10(1), 283-316. https://doi.org/10.14527/pegegog.2020.010
Fonger, N. L. (2019). Meaningfulness in representational fluency: An analytic lens for students’ creations, interpretations, and connections. Journal of Mathematical Behavior, (54), 100678. https://doi.org/10.1016/j.jmathb.2018.10.003
Gagani, R. F. M., & Misa, R. O. (2017). SOLO based-cognition levels of inductive reasoning in geometry. Alberta Journal of Educational Research, 63(4), 344-356. https://doi.org/10.11575/ajer.v63i4.56331
Goss, J. (2015). A method for describing the informal inferential reasoning of middle school students. [Unpublished doctoral dissertation]. Western Michigan University.
Groth, R. E., & Bergner, J. A. (2006). Preservice elementary teachers’ conceptual and procedural knowledge of mean, median, and mode. Mathematical Thinking & Learning: An International Journal, 8(1), 37-63. https://doi.org/10.1207/s15327833mtl0801_3
Hattie, J. A. C., & Brown, G. T. L. (2004). Cognitive processes in AsTTle: The SOLO taxonomy. Ministry of Education (New Zealand). https://www.yumpu.com/en/document/view/31644453/43-the-solo-taxonomy-2004pdf-e-asttle
Haynes, J. E. (2009). Qualitative analyses of a fundamental motor skill across the lifespan; linking practice and theory [Unpublished doctoral thesis]. University of New England, Australia, AU. https://hdl.handle.net/1959.11/3015
Holmes, K. (2005). Analysis of asynchronous online discussion using the SOLO taxonomy. Australian Journal of Educational & Developmental Psychology, 5, 117-127. https://files.eric.ed.gov/fulltext/EJ815606.pdf
Hook, P. (2016). First steps with SOLO taxonomy: Applying the model in your classroom. Essential Resources Educational Publishers Limited.
Huey, M. E. (2011). Characterizing middle and secondary preservice teachers’ change in inferential reasoning [Unpublished doctoral dissertation]. University of Missouri-Columbia.
Ilhan, M., & Cetin, B. (2016). The identification of the views of raters on standard rubrics and rubrics based on the SOLO taxonomy. Eğitimde Kuram ve Uygulama [Theory and Practice in Education], 12(1), 1-16.
Jimoyiannis, A. (2011). Using SOLO taxonomy to explore students’ mental models of the programming variable and the assignment statement. Themes in Science and Technology Education, 4(2), 53-74. http://earthlab.uoi.gr/theste/index.php/theste/article/view/99/69
Jurdak, M. E., & El Mouhayar, R. R. (2014). Trends in the development of student level of reasoning in pattern generalization tasks across grade level. Educational Studies in Mathematics, 85(1), 75-92. https://doi.org/10.1007/s10649-013-9494-2
Kaharuddin, A., & Hajeniati, N. (2020). An identification of students’ responses based on SOLO taxonomy in mathematics learning toward learning activities and learning outcomes. Al-Jabar: Jurnal Pendidikan Matematika [Al-Jabar: Journal of Mathematics Education], 11(2), 191-200. https://doi.org/10.24042/ajpm.v11i2.6270
Kamol, N., & Ban Har, Y. (2010). Upper primary school students’ algebraic thinking. In L. Sparrow, B. Kissane, & C. Hurst (Eds.), Shaping the future of mathematics education: Proceedings of the 33^rd Annual Conference of the Mathematics Education Research Group of Australasia (pp. 289-296). MERGA.
Lam, P., & Foong, Y.-Y. (1996). Rasch analysis of math SOLO taxonomy levels using hierarchical items in testlets [Report]. ERIC Document Reproduction Service No. ED 398271. https://files.eric.ed.gov/fulltext/ED398271.pdf
Lian, L. H., & Idris, N. (2006). Assessing algebraic solving ability of form four students. International Electronic Journal of Mathematics Education, 1(1), 55-76. https://doi.org/10.29333/iejme/171
Lian, L. H., Yew, W. T., & Idris, N. (2010). Superitem test: An alternative assessment tool to assess students’ algebraic solving ability. International Journal for Mathematics Teaching and Learning, 1-15. https://www.cimt.org.uk/journal/lian.pdf
Lucander, H., Bondemark, L., Brown, G., & Knutsson, K. (2010). The structure of observed learning outcome (SOLO) taxonomy: A model to promote dental students’ learning. European Journal of Dental Education, 14(3), 145-150. https://doi.org/10.1111/j.1600-0579.2009.00607.x
Mhlolo, M. K., & Schafer, M. (2013). Consistencies far beyond chance: An analysis of learner preconceptions of reflective symmetry. South African Journal of Education, 33(2), 1-17. https://doi.org/10.15700/saje.v33n2a686
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & Prisma Group (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
Mukuka, A., Balimuttajjo, S., & Mutarutinya, V. (2020). Applying the SOLO taxonomy in assessing and fostering students’ mathematical problem-solving abilities [Paper presentation]. The 28^th Annual Conference of the Southern African Association for Research in Mathematics, Science and Technology Education.
Mulbar, U., Rahman, A., & Ahmar, A. (2017). Analysis of the ability in mathematical problem-solving based on SOLO taxonomy and cognitive style. World Transactions on Engineering and Technology Education, 15(1). https://doi.org/10.26858/WTETEV15I1Y2017P6873
Mulligan, J., & Watson, J. (1998). A developmental multimodal model for multiplication and division. Mathematics Education Research Journal, 10(2), 61-86. https://doi.org/10.1007/BF03217343
Niemela, P., Mikkolainen, V., & Vuorinen, J. (2018). Compute mindlessly. Not! Map consciously. Universal Journal of Educational Research, 6(11), 2669-2678. https://doi.org/10.13189/ujer.2018.061133
Ozdemir, A. S., & Goktepe Yildiz, S. (2015). The analysis of elementary mathematics preservice teachers’ spatial orientation skills with SOLO model. Eurasian Journal of Educational Research, 61, 217-236. https://doi.org/10.1016/j.sbspro.2015.01.588
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hrobjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-ilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, 71. https://doi.org/10.1136/bmj.n71
Pegg, J. (1992). Students’ understanding of geometry: Theoretical perspectives. In B. Southwell (Ed.), Space: The first and final frontier: Proceedings of the Annual Conference of the Mathematics Education Research Group of Australasia (pp. 18-36). MERGA.
Pegg, J. (2010). Promoting the acquisition of higher order skills and understandings in primary and secondary mathematics [Paper presentation]. Australian Council for Education Research Conference, Melbourne.
Pegg, J., & Panizzon, D. (2007). Addressing changing assessment agendas: Impact of professional development on secondary mathematics teachers in NSW. Mathematics Teacher Education & Development, 9, 66-80. http://doi.org/10.1.1.496.4499
Pegg, J., & Tall, D. (2005). Using theory to advance our understandings of student cognitive development. International Group for the Psychology of Mathematics Education, 187.
Peters, S. A. (2010). Developing an understanding of variation: Ap statistics teachers’ perceptions and recollections of critical moments [Unpublished doctoral dissertation]. Pennsylvania State University, University Park, PA.
Pfannkuch, M. (2005). Characterizing year 11 students’ evaluation of a statistical process. Statistics Education Research Journal, 4(2), 5-26. https://doi.org/10.52041/serj.v4i2.512
Popay, J., Roberts, H., Sowden, A., Petticrew, M., Arai, L., Rodgers, M., Britten, N., Roen, K., & Duffy, S. (2006). Guidance on the conduct of narrative synthesis in systematic reviews: A product from the ESRC methods programme. Lancaster University. https://doi.org/10.13140/2.1.1018.4643
Putri, U., Mardiyana, M., & Saputro, D. (2017). How to analyze the students’ thinking levels based on SOLO taxonomy? Journal of Physics: Conference Series, 895, 012031. https://doi.org/10.1088/1742-6596/895/1/012031
Reading, C. (2004). Student description of variation while working with weather data. Statistics Education Research Journal, 3(2), 84-105. https://doi.org/10.52041/serj.v3i2.548
Rider, R. L. (2004). Using the SOLO taxonomy to evaluate student learning of function concepts in developmental algebra. North American Chapter of the International Group for the Psychology of Mathematics Education.
Ronda, E. (2015). Growth points in linking representations of function: A research-based framework. Educational Studies in Mathematics, 90(3), 303-319. https://doi.org/10.1007/s10649-015-9631-1
Rooney, C. (2012). How am I using inquiry-based learning to improve my practice and to encourage higher order thinking among my students of mathematics? Educational Journal of Living Theories, 5(2), 99-127.
Serow, P. (2007). Utilising the Rasch model to gain insight into students’ understandings of class inclusion concepts in geometry. Mathematics: Essential Research, Essential Practice, 2, 651-666.
Serow, P., Callingham, R., & Muir, T. (2019). Primary mathematics: Integrating theory with practice. Cambridge University Press. https://doi.org/10.1017/9781108665377
Sudihartinih, E. (2019). Facilitating mathematical understanding in three-dimensional geometry using the SOLO taxonomy. Erudio Journal of Educational Innovation, 6(1), 11-18. https://doi.org/10.18551/erudio.6-1.2
Sun, X. (2013). Assessment of prospective teachers’ multiple proof construction of a trapezoid area formula. New Waves, 16(1), 124-145.
Taplin, M. (1998). Preservice teachers’ problem-solving processes. Mathematics Education Research Journal, 10(3), 59-75. https://doi.org/10.1007/BF03217058
Thornton, A., & Lee, P. (2000). Publication bias in meta-analysis: Its causes and consequences. Journal of Clinical Epidemiology, 53(2), 207-216. https://doi.org/10.1016/s0895-4356(99)00161-4
Thouless, H., & Gifford, S. (2019). Dotty triangles. For the Learning of Mathematics, 39(2), 13-18.
Tosuncuoglu, I. (2018). Importance of assessment in ELT. Journal of Education and Training Studies, 6(9), 163-167. https://doi.org/10.11114/jets.v6i9.3443
Watson, J. M. (2002). Inferential reasoning and the influence of cognitive conflict. Educational Studies in Mathematics, 51(3), 225-256. https://doi.org/10.1023/A:1023622017006
Watson, J. M. (2007). The role of cognitive conflict in developing students’ understanding of average. Educational Studies in Mathematics, 65(1), 21-47. https://doi.org/10.1007/s10649-006-9043-3
Watson, J. M., & Moritz, J. B. (2001). Development of reasoning associated with pictographs: Representing, interpreting, and predicting. Educational Studies in Mathematics, 48(1), 47-81. https://doi.org/10.1023/A:1015594414565
Watson, J. M., & Moritz, J. B. (2003). Fairness of dice: A longitudinal study of students’ beliefs and strategies for making judgements. Journal for Research in Mathematics Education, 34(4), 270-304. https://doi.org/10.2307/30034785
Watson, J., Chick, H., & Callingham, R. (2014). Average: The juxtaposition of procedure and context. Mathematics Education Research Journal, 26(3), 477-502. https://doi.org/10.1007/s13394-013-0113-4
Wessels, H. M. (2007). Types and levels of data arrangement and representation in statistics as modeled by grade 4 to 7 learners [Unpublished doctoral dissertation]. University of South Africa.
Wongyai, P., & Kamol, N. (2004). A framework in characterizing lower secondary school students’ algebraic thinking. https://www.icme-organisers.dk/tsg09/PiyavadeeWongyai.pdf

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APA

Adeniji, S. M., Baker, P., & Schmude, M. (2022). Structure of the Observed Learning Outcomes (SOLO) model: A mixed-method systematic review of research in mathematics education. Eurasia Journal of Mathematics, Science and Technology Education, 18(6), em2119. https://doi.org/10.29333/ejmste/12087

Vancouver

Adeniji SM, Baker P, Schmude M. Structure of the Observed Learning Outcomes (SOLO) model: A mixed-method systematic review of research in mathematics education. EURASIA J Math Sci Tech Ed. 2022;18(6):em2119. https://doi.org/10.29333/ejmste/12087

AMA

Adeniji SM, Baker P, Schmude M. Structure of the Observed Learning Outcomes (SOLO) model: A mixed-method systematic review of research in mathematics education. EURASIA J Math Sci Tech Ed. 2022;18(6), em2119. https://doi.org/10.29333/ejmste/12087

Chicago

Adeniji, Saidat Morenike, Penelope Baker, and Martin Schmude. "Structure of the Observed Learning Outcomes (SOLO) model: A mixed-method systematic review of research in mathematics education". Eurasia Journal of Mathematics, Science and Technology Education 2022 18 no. 6 (2022): em2119. https://doi.org/10.29333/ejmste/12087

Harvard

Adeniji, S. M., Baker, P., and Schmude, M. (2022). Structure of the Observed Learning Outcomes (SOLO) model: A mixed-method systematic review of research in mathematics education. Eurasia Journal of Mathematics, Science and Technology Education, 18(6), em2119. https://doi.org/10.29333/ejmste/12087

MLA

Adeniji, Saidat Morenike et al. "Structure of the Observed Learning Outcomes (SOLO) model: A mixed-method systematic review of research in mathematics education". Eurasia Journal of Mathematics, Science and Technology Education, vol. 18, no. 6, 2022, em2119. https://doi.org/10.29333/ejmste/12087