有效能的教師依據學生個別差異進行教學調整，然而教師宜以哪些個別差異作為適性教學之依據，且隨時間依據不同個別差異之適性教學調整又如何影響學生的情意和認知成效，是當今適性教學研究亟需補足的缺口。本研究以成績、興趣和多元智能三種適性標的，反映學生於認知與情意面之學習結果、行為表現及發展潛能之個別差異，探討教師依據多元（三種）適性標的進行教學調整與學生數學學習成效之成長趨勢與交互影響。本研究採準實驗之不等組前後測設計，以方案成效評估者身分，針對108學年有無實施適性教學介入方案之實驗與對照兩組，三所國小三、四年級共31位教師和685位學生，進行教師教學調整問卷、學生學習情意問卷，與數學評量試卷之追蹤測量。應用階層線性模式（hierarchical linear model, HLM）之成長模型及偏最小平方結構方程模式（partial least squares structural equation modeling, PLS-SEM）之交互延宕縱貫模型等分析方法，三個研究問題的結果顯示：
　　（1）實施介入方案顯著提高實驗組教師適性力，教師增加依據適性標的─多元智能進行教學調整的頻率，依據適性標的─成績和興趣調整的頻率則先降後升。實驗組教師最終增加依據多元適性標的進行教學調整的頻率，對照組教師則無顯著增減。
　　（2）實驗組教師增加依據適性標的─多元智能調整的頻率、減少依據適性標的─成績調整的頻率，顯著提升實驗組學生的數學學習成就，且成長率優於對照組平均水準。實驗組教師依據低起始能力學生之適性標的進行教學調整，有助提升低起始能力學生的學習自信和學習感受，從而增進數學學習成就。
　　（3）教師增加依據適性標的進行教學調整的頻率，經過一個學年能橫斷直接且交互延宕地提升學生的數學情意成效與學習成就。教師持續增加適性教學調整的頻率時，學生學習成就呈現先降後升；較低的學習成就激發教師增加適性教學調整頻率，最終促進學生提升學習成就。當教師依據多元適性標的進行教學調整時，學生的數學情意成效與學習成就交互正向影響。
　　最後，說明本研究對適性教學研究於量化工具和縱貫實證研究方面之貢獻，並提出「適性－情意－成就模型」代表教師適性教學循環、學生學習迭代成長、師生認知情意交互影響之有效教與學動態系統，作為推動融合教育政策、適性教學介入、教師專業發展與師資培育等教育機關學校之參考。

Effective teachers adapt their methods to students' learning differences. However, the way these instructional adaptations affect students' affective and cognitive outcomes over time has not been explored much. Three types of adaptive targets—grades, interests, and multiple intelligences—were proposed in this study to represent individual differences related to learning outcomes, behavioral expression, and developmental potential across cognitive and affective domains. The study aimed to investigate the iterative and reciprocal predictions between teachers' instructional adaptations to multiple (three) adaptive targets and students' learning outcomes in mathematics. A quasi-experimental design was chosen to evaluate the effectiveness of an adaptive teaching intervention program, and a multi-method approach was employed to repeatedly measure teachers' instructional adaptations, students' affective outcomes, and mathematics achievements. This study involved an experimental group that implemented an adaptive teaching intervention program in the 2019 school year and a control group without intervention. A total of 31 teachers and 685 students from the 3rd and 4th grade of three primary schools were involved. Hierarchical linear models with growth modeling and partial least squares structural equation modeling with cross-lagged panel design were utilized to examine three research questions. The results of this study indicated that (1) The intervention program fostered the adaptability of the experimental group teachers. Over time, they increased their frequency of instructional adaptation to the "multiple intelligences" target. Meanwhile, the frequency of instructional adaptation to "grades" and "interests" first decreased and then increased over time. Ultimately, the experimental group teachers significantly increased the frequency of instructional adaptation to multiple adaptive targets, whereas no significant differences were found in the control group teachers. (2) The experimental group students, whose teachers increased the frequency of instructional adaptation to "multiple intelligences" and decreased it to "grades," showed significantly higher growth rates of mathematical achievements compared to the control group students. Low-achieving students in the experimental group showed significantly higher growth rates of learning confidence and learning perception toward adaptive teaching, thereby increasing their growth rates of mathematical achievements. (3) The increase in frequency of instructional adaptation showed positive contemporary direct effects and longitudinal cross-lagged effects on students' affective outcomes (learning interests, learning confidence, and learning perception) and mathematical achievements throughout the school year. While teachers increased the frequency of their instructional adaptation, students' mathematical achievements declined at the end of the first semester but rose at the end of the second semester. For low-achieving students, teachers increased the frequency of instructional adaptation to multiple adaptive targets. Furthermore, this increase generated positive reciprocal effects between students' affective outcomes and mathematical achievements. In conclusion, this study makes valuable contributions to adaptive teaching research by developing quantitative instruments, providing longitudinal empirical evidence, and proposing an adaptability-affect-achievement model. The model represents a dynamic system of effective teaching and learning that consists of adaptive teaching cycles, iterative learning outcomes, and reciprocal interactions between teachers and students. Pedagogical implications for inclusive education, intervention programs, professional development, and teacher education to promote teacher adaptability and student learning outcomes are discussed.

中文部分
王玉品、徐偉民（2009）：一位國小教師面對數學教學改革的抗拒和改變。科學教育學刊，17（3），233‒253。https://doi.org/10.6173/CJSE.2009.1703.02
王為國（2015）：多元智能教育理論與實務（三版）。心理。
任宗浩、譚克平、張立民（2011）：二階段分層叢集抽樣的設計效應估計。教育科學研究期刊，56（1），33‒65。https://doi.org/10.3966/2073753X2011035601002
余民寧（2009）：試題反應理論IRT及其應用。心理。
余民寧、韓珮華（2009）：教學方式對數學學習興趣與數學成就之影響：以TIMSS 2003台灣資料爲例。測驗學刊，56（1），19‒48。https://doi.org/10.7108/PT.200903.0019
李君柔、王美娟（2013）：個人特質、家庭環境、教師教學與學校背景對八年級學生數學成就之影響。臺北市立教育大學學報，44（1），51‒84。https://doi.org/10.6336/JUTe/2013.44(1)3
林奕宏、張景媛（2001）：多元智能與問題解決整合型教學模式對國小學生數學學習表現之影響。教育心理學報，33（1），1–30。https://doi.org/10.6251/BEP.20010225
林素微（2018）：數學課室教師支持與學生數學素養關聯探討：以PISA2012臺灣資料為例。臺灣數學教師，39（1），1‒17。https://doi.org/10.6610/TJMT.201804_39(1).0001
林素微、吳正新、洪碧霞（2013）：課室教學活動對數學學習成就解釋力之探討：以 TIMSS 2007 臺灣資料為例。測驗統計年刊，21（1），41‒59。
邱皓政（2017）：多層次模式與縱貫資料分析：Mplus8解析應用。五南。
邱皓政（2018）：測驗原理與量表發展技術（二版）。雙葉書廊。
洪碧霞、林素微（2017）：認知本位電腦化學習評量系統的應用效益與拓展方向：以攜手計畫課後扶助方案科技化評量系統為例。測驗學刊，64（4），313‒339。
張玉茹、江芳盛（2013）：師生關係、學習動機與數學學業成就模式之驗證－以 PISA2003資料庫為例。測驗統計年刊，21（2），91‒121。
張芬芬、王瓊英（2018）：新北市國小英語教師適性教學的觀點與實踐之調查研究。教育研究月刊，285，69‒89。https://doi.org/10.3966/168063602018010285005
張俊彥、任宗浩、李哲迪、林碧珍、張美玉、曹博盛、楊文金（2018）：結論與建議。載於張俊彥（主編），國際數學與科學教育成就趨勢調查2015國家報告（470‒485）。師大科教中心。引自網站：https://www.sec.ntnu.edu.tw/timss2015/05-resault.aspx
教育部（2014）：十二年國民基本教育課程綱要總綱。引自網站：https://www.k12ea.gov.tw
陳冠銘、任宗浩（2018）：TIMSS 2015 的評量架構。載於張俊彥（主編），國際數學與科學教育成就趨勢調查2015國家報告（14‒42）。師大科教中心。引自網站：https://www.sec.ntnu.edu.tw/timss2015/05-resault.aspx
陳柏熹（2011）：心理與教育測驗-測驗編製理論與實務。精策教育。
曾明基（2017）：進行多層次建模最小可行的樣本數建議：貝氏模擬取向。教育研究與發展期刊，13（4），1‒26。https://doi.org/10.3966/181665042017121304001
曾芬蘭、游羽萱、蔡逸凡、陳柏熹（2019）：國中教育會考英語科聽力測驗實施的回沖效應初探。教育科學研究期刊，64（2），219–252。https://doi.org/10.6209/JORIES.201906_64(2).0008
溫福星、邱皓政（2009）：多層次模型方法論：階層線性模式的關鍵議題與試解。臺大管理論叢，19（2），263‒293。https://doi.org/10.6226/NTURM2009.19.2.263
葛湘瑋（2004）：應用線性混合效果模式於建立多層縱向資料的模式之實例研究。教育與心理研究，27（2），399‒419。
劉春初、王澤宇、陳威仁（2019）：國民中學學生數學成就表現之跨國比較：以 TIMSS 為例。測驗學刊，66（1），1‒26。
劉湘川、李文忠（1995）：無參數試題反應理論之等化模式應用在垂直等化之效益研究。測驗統計年刊，3，37‒51。
蔡雅薰、洪榮昭、余信賢（2019）：國際華語教師學科教學知識問卷之編製與教師教學能力素養落差分析。測驗學刊，66（4），403‒428。
鄭博真（2006）：台灣地區多元智能研究之回顧與展望：以碩博士學位論文為例。華醫學報，24，159–182。
鄭鈐華、吳昭容（2013）：與八年級課程同步實施的數學補救教學：成效與反思。臺東大學教育學報，24（2），1‒31。https://doi.org/10.3966/102711202013122402001
龔心怡、李靜儀（2016）：國中學生數學自我概念與數學學業成就相互效果模式之縱貫研究—性別差異與城鄉差距之觀點。科學教育學刊，24（S），511‒536。https://doi.org/10.6173/CJSE.2016.24S.04
西文部分
Altintas, E., & Ozdemir, A. S. (2015). The effect of developed differentiation approach on the achievements of the students. Eurasian Journal of Educational Research, 61, 199–216. https://doi.org/10.14689/ejer.2015.61.11
Anderman, E. M. (2020). Achievement motivation theory: Balancing precision and utility. Contemporary Educational Psychology, 61, Article 101864. https://doi.org/10.1016/j.cedpsych.2020.101864
Arens, A. K., Frenzel, A. C., & Goetz, T. (2020). Self-concept and self-efficacy in math: longitudinal interrelations and reciprocal linkages with achievement. The Journal of Experimental Education, 1–19. https://doi.org/10.1080/00220973.2020.1786347
Armstrong, T. (2009). Multiple intelligences in the classroom (3rd ed.). ASCD.
Artzt, A. F., & Armour-Thomas, E. (1998). Mathematics teaching as problem solving: A framework for studying teacher metacognition underlying instructional practice in mathematics. Instructional Science, 26, 5–25. https://doi.org/10.1007/978-94-017-2243-8_7
Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its component processes. In K. Spence & J. Spence (Eds.), The psychology of learning and motivation (Vol. 2, pp. 89–195). Academic Press. https://doi.org/10.1016/S0079-7421(08)60422-3
Baddeley, A. (1989). The psychology of remembering and forgetting. In T. Butler (Ed.), Memory: History, culture and the mind (pp. 33–60). Basil Blackwell.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall.
Belfi, B., Goos, M., De Fraine, B., & Van Damme, J. (2012). The effect of class　composition by gender and ability on secondary school students’ school well-being and academic self-concept: A literature review. Educational Research　Review, 7(1), 62–74. https://doi.org/10.1016/j.edurev.2011.09.002
Berliner, D. C. (1986). In pursuit of the expert pedagogue. Educational Researcher, 15(7), 5–13. https://doi.org/10.3102/0013189X015007007
Bernard, R. M., Borokhovski, E., Schmid, R. F., Waddington, D. I., & Pickup, D. I. (2019). Twenty-first century adaptive teaching and individualized learning operationalized as specific blends of student-centered instructional events: A systematic review and meta-analysis. Campbell Systematic Reviews, 15, 1–35. https://doi.org/10.1002/cl2.1017
Bock, R. D., & Mislevy, R. J. (1982). Adaptive EAP estimation of ability in a microcomputer environment. Applied Psychological Measurement, 6(4), 431–444. https://doi.org/10.1177/014662168200600405
Borich, G. D. (2014). Effective teaching methods: Research-based practice (8th ed.). Pearson.
Boykin, A. W. (2000). The talent development model of schooling: Placing students at promise for academic success. Journal of Education for Students Placed at Risk, 5(1 & 2), 3–25. https://doi.org/10.1080/10824669.2000.9671377
Brühwiler, C., & Blatchford, P. (2011). Effects of class size and adaptive teaching competency on classroom processes and academic outcome. Learning and Instruction, 21, 95–108. https://doi.org/10.1016/j.learninstruc.2009.11.004
Burns, E. C., Martin, A. J., & Collie, R. J. (2018). Adaptability, personal best (PB) goals setting, and gains in students’ academic outcomes: A longitudinal examination from a social cognitive perspective. Contemporary Educational Psychology, 53, 57–72. https://doi.org/10.1016/j.cedpsych.2018.02.001
Campbell, L., Campbell, B., & Dickerson, D. (2004). Teaching and learning through multiple intelligences. Pearson.
Cantor, P., Osher, D., Berg, J., Steyer, L., & Rose, T. (2019). Malleability, plasticity, and individuality: How children learn and develop in context1. Applied Developmental Science, 23(4), 307–337. https://doi.org/10.1080/10888691.2017.1398649
Chi, M. T., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145–182. https://doi.org/10.1207/s15516709cog1302_1
Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with longitudinal data: questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112(4), 558–577. https://doi.org/10.1037/0021-843X.112.4.558
Collie, R. J., Granziera, H., Martin, A. J., Burns, E. C., & Holliman, A. J. (2020). Adaptability among science teachers in schools: A multi-nation examination of its role in school outcomes. Teaching and Teacher Education, 95, Article 103148. https://doi.org/10.1016/j.tate.2020.103148
Collie, R. J., & Martin, A. J. (2016). Adaptability: An important capacity for effective teachers. Educational Practice and Theory, 38, 27–39. https://doi.org/10.7459/ept/38.1.03
Collie, R. J., & Martin, A. J. (2017). Teachers' sense of adaptability: Examining links with perceived autonomy support, teachers' psychological functioning, and students' numeracy achievement. Learning and Individual Differences, 55, 29–39. https://doi.org/10.1016/j.lindif.2017.03.003
Conn, K. M. (2017). Identifying effective education interventions in sub-Saharan Africa: A meta-analysis of impact evaluations. Review of Educational Research, 87(5), 863–898. https://doi.org/10.3102/0034654317712025
Connor, C. M., Mazzocco, M. M., Kurz, T., Crowe, E. C., Tighe, E. L., Wood, T. S., & Morrison, F. J. (2018). Using assessment to individualize early mathematics instruction. Journal of School Psychology, 66, 97–113. https://doi.org/10.1016/j.jsp.2017.04.005
Corno, L. (2008). On teaching adaptively. Educational Psychologist, 43(3), 161–173. https://doi.org/10.1080/00461520802178466
Coubergs, C., Struyven, K., Vanthournout, G., & Engels, N. (2017). Measuring teachers’ perceptions about differentiated instruction: The DI-Quest instrument and model. Studies in Educational Evaluation, 53, 41–54. https://doi.org/10.1016/j.stueduc.2017.02.004
Council of Chief State School Officers. (2011). The Interstate New Teacher Assessment and Support Consortium (InTASC) model core teaching standards: A resource for state dialogue. https://ccsso.org/resource-library/intasc-model-core-teaching-standards
Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and instructional methods: A handbook for research on interactions. Irvington.
Cumming-Potvin, W. (2007). Scaffolding, multiliteracies, and reading circles. Canadian Journal of Education, 30, 483–507. https://doi.org/10.2307/20466647
Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2020). Implications for educational practice of the science of learning and development. Applied Developmental Science, 24(2), 97–140. https://doi.org/10.1080/10888691.2018.1537791
Davidson, J. E., & Kemp, I. A. (2011). Contemporary models of intelligence. In R. J. Sternberg & S. B. Kaufman (Eds.), Cambridge handbook of intelligence (pp. 58–84). Cambridge University Press.
Davis, K., Christodoulou, J., Seider, S., & Gardner, H. (2011). The theory of multiple intelligences. In R. J. Sternberg & S. B. Kaufman (Eds.), Cambridge handbook of intelligence (pp. 485–503). Cambridge University Press.
Denton, C. A., Swanson, E. A., & Mathes, P. G. (2007). Assessment-based instructional coaching provided to reading intervention teachers. Reading and Writing, 20, 569–590. https://doi.org/10.1007/s11145-007-9055-0
Deunk, M. I., Smale-Jacobse, A. E., de Boer, H., Doolaard, S., & Bosker, R. J. (2018). Effective differentiation practices: A systematic review and meta-analysis of studies on the cognitive effects of differentiation practices in primary education. Educational Research Review, 24, 31–54. https://doi.org/10.1016/j.edurev.2018.02.002
Du Toit, M. (Ed.). (2003). IRT from SSI: Bilog-MG, multilog, parscale, testfact. Scientific Software International.
Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, Article 101859. https://doi.org/10.1016/j.cedpsych.2020.101859
Eriksson, K., Helenius, O., & Ryve, A. (2019). Using TIMSS items to evaluate the effectiveness of different instructional practices. Instructional Science, 47(1), 1–18. https://doi.org/10.1007/s11251-018-9473-1
Fennema, E., Franke, M. L., Carpenter, T. P., & Carey, D. A. (1993). Using children's mathematical knowledge in instruction. American Educational Research Journal, 30, 555–583. https://doi.org/10.3102/00028312030003555
Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50. https://doi.org/10.2307/3151312
Gallagher, M. A., Parsons, S. A., & Vaughn, M. (2020). Adaptive teaching in mathematics: A review of the literature. Educational Review. Advance online publication. https://doi.org/10.1080/00131911.2020.1722065
Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. Basic Books.
Gardner, H. (1993). Multiple intelligences: The theory in practice. Basic Books.
Gardner, H. (2000). Intelligence reframed: Multiple intelligences for the 21st century. Basic Books.
Gardner, H. (2006). Multiple intelligences: New horizons. Basic Books.
Gardner, H., Krechevsky, M. Sternberg, R. J., & Okagaki, L. (1994). Intelligence in context: Enhancing students’ practical intelligence for school. In K. McGilly (Ed.), Classroom lessons: Integrating cognitive theory and classroom practice (pp. 105–127). Bradford Books.
Garn, A. C., Morin, A. J. S., & Lonsdale, C. (2019). Basic psychological need satisfaction toward learning: A longitudinal test of mediation using bifactor exploratory structural equation modeling. Journal of Educational Psychology, 111(2), 354–372. https://doi.org/10.1037/edu0000283
Ghasemy, M., Teeroovengadum, V., Becker, J. M., & Ringle, C. M. (2020). This fast car can move faster: A review of PLS-SEM application in higher education research. Higher Education, 80(6), 1121–1152. https://doi.org/10.1007/s10734-020-00534-1
Glaser, R. (1977). Adaptive education: Individual diversity and learning. Holt, Rinehart and Winston.
Graham, S., Morphy, P., Harris, K. R., Fink-Chorzempa, B., Saddler, B., Moran, S., & Mason, L. (2008). Teaching spelling in the primary grades: A national survey of instructional practices and adaptations. American Educational Research Journal, 45, 796–825. https://doi.org/10.3102/0002831208319722
Grigg, S., Perera, H. N., McIlveen, P., & Svetleff, Z. (2018). Relations among math self efficacy, interest, intentions, and achievement: A social cognitive perspective. Contemporary Educational Psychology, 53, 73–86. https://doi.org/10.1016/j.cedpsych.2018.01.007
Guay, F., Stupnisky, R., Boivin, M., Japel, C., & Dionne, G. (2019). Teachers’ relatedness with students as a predictor of students’ intrinsic motivation, self-concept, and reading achievement. Early Childhood Research Quarterly, 48, 215–225. https://doi.org/10.1016/j.ecresq.2019.03.005
Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2019). Multivariate data analysis (8th ed). Cengage Learning.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage.
Hair, J.F., Risher, J.J., Sarstedt, M., & Ringle, C.M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hattie, J. (1985). Methodology review: Assessing unidimensionality of tests and items. Applied Psychological Measurement, 9(2), 139–164. https://doi.org/10.1177/014662168500900204
Heck, R. H., & Thomas, S. L. (2020). An introduction to multilevel modeling techniques: MLM and SEM approaches. Routledge.
Henseler, J., Ringle, C.M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hoffman, J. V., & Duffy, G. G. (2016). Does thoughtfully adaptive teaching actually exist? A challenge to teacher educators. Theory Into Practice, 55, 172–179. https://doi.org/10.1080/00405841.2016.1173999
Holland, P. W., & Dorans, N. J. (2006). Linking and equating. In R. L. Brennan (Ed.), Educational measurement (4th ed., pp. 187–220). Praeger.
Holzberger, D., Philipp, A., & Kunter, M. (2014). Predicting teachers’ instructional behaviors: The interplay between self-efficacy and intrinsic needs. Contemporary Educational Psychology, 39(2), 100–111. https://doi.org/10.1016/j.cedpsych.2014.02.001
Hooper, M., Mullis, I. V. S., & Martin, M. O. (2013). TIMSS 2015 context questionnaire framework. In I. V. S. Mullis, & M. O. Martin (Eds.), TIMSS 2015 assessment frameworks (pp. 61–82). TIMSS & PIRLS International Study Center, Boston College. https://timssandpirls.bc.edu/timss2015/frameworks.html
Hoover, J. J., & Patton, J. R. (2005). Curriculum adaptation for students with learning and behavior problem: Principles and practices. (3rd ed.). PRO-ED.
Hox, J. J., Moerbeek, M., & Van de Schoot, R. (2017). Multilevel analysis: Techniques and applications (3rd ed.). Routledge.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. https://doi.org/10.1080/10705519909540118
Jager, L., Denessen, E., Cillessen, A. H., & Meijer, P. C. (2021). Sixty seconds about each student–studying qualitative and quantitative differences in teachers’ knowledge and perceptions of their students. Social Psychology of Education, 24(1), 1–35. https://doi.org/10.1007/s11218-020-09603-w
Jensen, M. T., Solheim, O. J., & Idsøe, E. M. C. (2019). Do you read me? Associations between perceived teacher emotional support, reader self-concept, and reading achievement. Social Psychology of Education, 22(2), 247–266. https://doi.org/10.1007/s11218-018-9475-5
Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Research, 44(3), 486–507. https://doi.org/10.1177%2F0049124114543236
Kincade, L., Cook, C., & Goerdt, A. (2020). Meta-analysis and common practice elements of universal approaches to improving student-teacher relationships. Review of Educational Research, 90(5), 710–748. https://doi.org/10.3102/0034654320946836
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational psychologist, 41(2), 75–86. https://doi.org/10.1207/s15326985ep4102_1
Kitsantas, A., Cleary, T. J., Whitehead, A., & Cheema, J. (2020). Relations among classroom context, student motivation, and mathematics literacy: A social cognitive perspective. Metacognition and Learning. Advance online publication. https://doi.org/10.1007/s11409-020-09249-1
Kiuru, N., Nurmi, J. E., Leskinen, E., Torppa, M., Poikkeus, A. M., Lerkkanen, M. K., & Niemi, P. (2015). Elementary school teachers adapt their instructional support according to students’ academic skills: A variable and person-oriented approach. International Journal of Behavioral Development, 39(5), 391–401. https://doi.org/10.1177/0165025415575764
Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Kolen, M. J., & Brennan, R. L. (2014). Test equating, scaling, and linking: Methods and practices (3rd ed.). Springer Science & Business Media. https://doi.org/10.1007/978-1-4939-0317-7
König, J., Bremerich-Vos, A., Buchholtz, C., & Glutsch, N. (2020). General pedagogical knowledge, pedagogical adaptivity in written lesson plans, and instructional practice among preservice teachers. Journal of Curriculum Studies, 52(6), 800–822. https://doi.org/10.1080/00220272.2020.1752804
Kornhaber, M., Fierros, E., & Veenema, S. (2004). Multiple intelligences: Best ideas from research and practice. Allyn & Bacon.
Korpershoek, H., Harms, T., de Boer, H., van Kuijk, M., & Doolaard, S. (2016). A meta-analysis of the effects of classroom management strategies and classroom management programs on students’ academic, behavioral, emotional, and motivational Outcomes. Review of Educational Research, 86(3), 643‒680. https://doi.org/10.3102%2F0034654315626799
Krämer, S., Möller, J., & Zimmermann, F. (2021). Inclusive education of students with general learning difficulties: A meta-analysis. Review of Educational Research, Advance online publication. https://doi.org/10.3102/0034654321998072
Lavrijsen, J., Vansteenkiste, M., Boncquet, M., & Verschueren, K. (2021). Does motivation predict changes in academic achievement beyond intelligence and personality? A multitheoretical perspective. Journal of Educational Psychology. Advance online publication. https://doi.org/10.1037/edu0000666
Lazarides, R., & Buchholz, J. (2019). Student-perceived teaching quality: How is it related to different achievement emotions in mathematics classrooms?. Learning and Instruction, 61, 45–59. https://doi.org/10.1016/j.learninstruc.2019.01.001
Lee, J., & Stankov, L. (2018). Non-cognitive predictors of academic achievement: Evidence from TIMSS and PISA. Learning and Individual Differences, 65, 50–64. https://doi.org/10.1016/j.lindif.2018.05.009
Lei, H., Cui, Y., & Chiu, M. M. (2018). The relationship between teacher support and students' academic emotions: A meta-analysis. Frontiers in Psychology, 8, Article 2288. https://doi.org/10.3389/fpsyg.2017.02288
Leikin, R., & Dinur, S. (2007). Teacher flexibility in mathematical discussion. Journal of Mathematical Behavior, 26, 328–347. https://doi.org/10.1016/j.jmathb.2007.08.001
Levine, M. (2003). Celebrating diverse minds. Educational Leadership, 61 (2), 12–18.
Liang, X., Yang, Y., & Huang, J. (2018). Evaluation of structural relationships in autoregressive cross-lagged models under longitudinal approximate invariance: A Bayesian analysis. Structural Equation Modeling: A Multidisciplinary Journal, 25(4), 558–572. https://doi.org/10.1080/10705511.2017.1410706
Lin, H. M., Lee, M. H., Liang, J. C., Chang, H. Y., Huang, P. C., & Tsai, C. C. (2020). A review of using partial least square structural equation modeling in e-learning research. British Journal of Educational Technology, 51(4), 1354–1372. https://doi.org/10.1111/bjet.12890
Lin-Siegler, X., Dweck, C. S., & Cohen, G. L. (2016). Instructional interventions that motivate classroom learning. Journal of Educational Psychology, 108(3), 295–299. https://dx.doi.org/10.1037/edu0000124
Little, T. D. (2013). Longitudinal structural equation modeling. Guilford press.
Livingston, S. A. (2004). Equating test scores (without IRT). Educational Testing Service.
Lord, F. M. (1980). Applications of item response theory to practical testing problems. Routledge.
Loughland T. (2019) Looking forward: Next steps for teacher adaptive practice research. In Teacher adaptive practices (pp. 81–89). Springer, Singapore. https://doi.org/10.1007/978-981-13-6858-5_6
Loughland, T., & Alonzo, D. (2018). Teacher adaptive practices: Examining links with teacher self-efficacy, perceived autonomy support and teachers’ sense of adaptability. Educational Practice and Theory, 40(2), 55–70. https://doi.org/10.7459/ept/40.2.04
Lovett, M. W., Lacerenza, L., De Palma, M., Benson, N. J., Steinbach, K. A., & Frijters, J. C. (2008). Preparing teachers to remediate reading disabilities in high school: What is needed for effective professional development? Teaching and Teacher Education, 24, 1083–1097. https://doi.org/10.1016/j.tate.2007.10.005
Lutz, S. L., Guthrie, J. T., & Davis, M. H. (2006). Scaffolding for engagement in elementary school reading instruction. Journal of Educational Research, 100(1), 3–20. https://doi.org/10.3200/JOER.100.1.3-20
MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. Taylor & Francis Group.
Marsh, H. W., Pekrun, R., Murayama, K., Arens, A. K., Parker, P. D., Guo, J., & Dicke, T. (2018). An integrated model of academic self-concept development: Academic self-concept, grades, test scores, and tracking over 6 years. Developmental Psychology, 54(2), Article 263. https://doi.org/10.1037/dev0000393
Marsh, H. W., Trautwein, U., Lüdtke, O., Köller, O., & Baumert, J. (2005). Academic self‐concept, interest, grades, and standardized test scores: Reciprocal effects models of causal ordering. Child Development, 76(2), 397–416. https://doi.org/10.1111/j.1467-8624.2005.00853.x
McNeish, D. M., & Stapleton, L. M. (2016). Modeling clustered data with very few clusters. Multivariate Behavioral Research, 51(4), 495‒518. https://doi.org/10.1080/00273171.2016.1167008
McNeish, D.M, Stapleton, L. M., & Silverman, R. D. (2017). On the unnecessary ubiquity of hierarchical linear modeling. Psychological Methods, 22(1), 114‒140. https://doi.org/10.1037/met0000078
Mok, M. M. C., Zhu, J., & Law, C. L. K. (2017). Cross-lagged cross-subject bidirectional predictions among achievements in mathematics, English language and Chinese language of school children. Educational Psychology, 37(10), 1259‒1280. https://doi.org/10.1080/01443410.2017.1334875
Möller, J., Zitzmann, S., Helm, F., Machts, N., & Wolff, F. (2020). A meta-analysis of relations between achievement and self-concept. Review of Educational Research, 90(3), 376–419. https://doi.org/10.3102/0034654320919354
Moran, S., Kornhaber, M., & Gardner, H. (2006). Orchestrating multiple intelligences. Educational Leadership, 64(1), 22–27.
Muenks, K., & Miele, D. B. (2017). Students’ thinking about effort and ability: The role of developmental, contextual, and individual difference factors. Review of Educational Research, 87(4), 707‒735. https://doi.org/10.3102%2F0034654316689328
Muthén, L.K., & Muthén, B.O. (2017). Mplus user's guide (8th ed.). Muthén & Muthén.
National Research Council. (2000). How people learn: Brain, mind, experience, and school: Expanded edition. National Academies Press.
Olivier, E., Archambault, I., De Clercq, M., & Galand, B. (2019). Student self-efficacy, classroom engagement, and academic achievement: Comparing three theoretical frameworks. Journal of Youth and Adolescence, 48(2), 326–340. https://doi.org/10.1007/s10964-018-0952-0
Paivio, A. (1969). Mental imagery in associative learning and memory. Psychological Review, 76(3), 241–263. https://doi.org/10.1037/h0027272
Park, D., Gunderson, E. A., Tsukayama, E., Levine, S. C., & Beilock, S. L. (2016). Young children’s motivational frameworks and math achievement: Relation to teacher-reported instructional practices, but not teacher theory of intelligence. Journal of Educational Psychology, 108(3), 300–313. https://doi.org/10.1037/edu0000064
Parsons, A. W., Ankrum, J. W., & Morewood, A. (2016). Professional development to promote teacher adaptability. Theory Into Practice, 55(3), 250–258. https://doi.org/10.1080/00405841.2016.1173995
Parsons, S. A., Vaughn, M., Scales, R. Q., Gallagher, M. A., Parsons, A. W., Davis, S. G., & Allen, M. (2018). Teachers' instructional adaptations: A research synthesis. Review of Educational Research, 88(2), 205–242. https://doi.org/10.3102/0034654317743198
Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9
Pekrun, R., Lichtenfeld, S., Marsh, H. W., Murayama, K., & Goetz, T. (2017). Achievement emotions and academic performance: Longitudinal models of reciprocal effects. Child Development, 88(5), 1653–1670. https://doi.org/10.1111/cdev.12704
Pekrun, R., Murayama, K., Marsh, H. W., Goetz, T., & Frenzel, A. C. (2019). Happy fish in little ponds: Testing a reference group model of achievement and emotion. Journal of Personality and Social Psychology, 117(1), 166–185. https://doi.org/10.1037/pspp0000230
Pellegrini, M., Lake, C., Neitzel, A., & Slavin, R. E. (2021). Effective programs in elementary mathematics: A meta-analysis. AERA Open, 7, 1–29. https://doi.org/10.1177/2332858420986211
Peterson, P. L., Marx, R. W., & Clark, C. M. (1978). Teacher planning, teacher behavior, and student achievement. American Educational Research Journal, 15, 417–432. https://doi.org/10.3102%2F00028312015003417
Piaget, J. (1952). The language and thought of the child. Routledge and Kegan-Paul.
Piaget, J. (1964). The moral judgment of the child. Free Press.
Pickering, D., Blaszczynski, A., & Gainsbury, S. M. (2020). Development and psychometric evaluation of the Recovery Index for Gambling Disorder (RIGD). Psychology of Addictive Behaviors. Advance online publication. https://doi.org/10.1037/adb0000676
Prast, E. J., Van de Weijer-Bergsma, E., Kroesbergen, E. H., & Van Luit, J. E. (2018). Differentiated instruction in primary mathematics: Effects of teacher professional development on student achievement. Learning and Instruction, 54, 22–34. https://doi.org/10.1016/j.learninstruc.2018.01.009
Prast, E. J., Van de Weijer-Bergsma, E., Miočević, M., Kroesbergen, E. H., & Van Luit, J. E. (2018). Relations between mathematics achievement and motivation in students of diverse achievement levels. Contemporary Educational Psychology, 55, 84–96. https://doi.org/10.1016/j.cedpsych.2018.08.002
Prast, E. J., Weijer-Bergsma, E., Kroesbergen, E. H., & Van Luit, J. E. (2015). Readiness-based differentiation in primary school mathematics: Expert recommendations and teacher self-assessment. Frontline Learning Research, 3(2), 90–116. https://doi.org/10.14786/flr.v3i2.163
Prince, S. E., Tsukiura, R., & Cabeza, R. (2007). Distinguishing the neural correlates of episodic memory encoding and semantic memory retrieval. Psychological Science, 18(2), 144–151. https://doi.org/10.1111/j.1467-9280.2007.01864.x
Puzio, K., Colby, G. T., & Algeo-Nichols, D. (2020). Differentiated literacy instruction: boondoggle or best practice? Review of Educational Research, 90(4), 459–498. https://doi.org/10.3102/0034654320933536
Quin, D. (2017). Longitudinal and contextual associations between teacher–student relationships and student engagement: A systematic review. Review of Educational Research, 87(2), 345‒387. https://doi.org/10.3102/0034654316669434
Rakes, C. R., Valentine, J. C., McGatha, M. B., & Ronau, R. N. (2010). Methods of instructional improvement in algebra: A systematic review and meta-analysis. Review of Educational Research, 80(3), 372‒400. https://doi.org/10.3102/0034654310374880
Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Sage.
Raudenbush, S. W., Bryk, A., Cheong, Y. F., Congdon, R., & Du Toit, M. (2011). HLM 7: Linear and nonlinear modeling. Scientific Software International.
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3 [Computer software]. SmartPLS GmbH. https://www.smartpls.com
Rodriguez, A. J. (2004). Teachers’ resistance to ideological and pedagogical change: Definitions, theoretical framework, and significance. In Preparing mathematics and science teachers for diverse classrooms (pp. 17–32). Routledge.
Roos, D., & Hahn, R. (2017). Does shared consumption affect consumers' values, attitudes, and norms? A panel study. Journal of Business Research, 77, 113–123. https://doi.org/10.1016/j.jbusres.2017.04.011
Rowan, L., Bourke, T., L’Estrange, L., Lunn Brownlee, J., Ryan, M., Walker, S., & Churchward, P. (2021). How does initial teacher education research frame the challenge of preparing future teachers for student diversity in schools? A systematic review of literature. Review of Educational Research, 91(1), 112–158. https://doi.org/10.3102/0034654320979171
Roy, A., Guay, F., & Valois, P. (2013). Teaching to address diverse learning needs: Development and validation of a differentiated instruction scale. International Journal of Inclusive Education, 17(11), 1186–1204. https://doi.org/10.1080/13603116.2012.743604
Roy, A., Guay, F., & Valois, P. (2015). The big-fish–little-pond effect on academic self-concept: The moderating role of differentiated instruction and individual achievement. Learning and Individual Differences, 42, 110–116. https://doi.org/10.1016/j.lindif.2015.07.009
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, Article 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
Schipper, T. M., van der Lans, R. M., de Vries, S., Goei, S. L., & van Veen, K. (2020). Becoming a more adaptive teacher through collaborating in Lesson Study? Examining the influence of lesson study on teachers’ adaptive teaching practices in mainstream secondary education. Teaching and Teacher Education, 88, Article 102961. https://doi.org/10.1016/j.tate.2019.102961
Schirduan, V., & Case, K. (2004). Mindful curriculum leadership for students with attention deficit hyperactivity disorder: Leading in elementary schools by using multiple intelligences theory (SUMIT). Teachers College Record, 106(1), 87–95.
Schmidt, H. G., Loyens, S. M., Van Gog, T., & Paas, F. (2007). Problem-based learning is compatible with human cognitive architecture: Commentary on Kirschner, Sweller, and Clark (2006). Educational psychologist, 42(2), 91–97. https://doi.org/10.1080/00461520701263350
Schunk, D. H., & DiBenedetto, M. K. (2020). Motivation and social cognitive theory. Contemporary Educational Psychology, 60, Article 101832. https://doi.org/10.1016/j.cedpsych.2019.101832
Scull, J. A., & Bianco, J. L. (2008). Successful engagement in an early literacy intervention. Journal of Early Childhood Literacy, 8, 123–150. https://doi.org/10.1177%2F1468798408091852
Sewasew, D., Schroeders, U., Schiefer, I. M., Weirich, S., & Artelt, C. (2018). Development of sex differences in math achievement, self-concept, and interest from grade 5 to 7. Contemporary Educational Psychology, 54, 55–65. https://doi.org/10.1016/j.cedpsych.2018.05.003
Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4–14. https://doi.org/10.3102%2F0013189X015002004
Shulman, L. S. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Review, 57(1), 1–23. https://doi.org/10.17763/haer.57.1.j463w79r56455411
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford University Press.
Slavin, R. E. (2018). Educational psychology: Theory and practice (12th ed.). Pearson.
Slavin, R., & Lake, C. (2008). Effective programs in elementary mathematics: A best-evidence synthesis. Review of Educational Research, 78(3), 427–515. https://doi.org/10.3102/0034654308317473
Slavin, R. E., Lake, C., & Groff, C. (2009). Effective programs in middle and high school mathematics: A best-evidence synthesis. Review of Educational Research, 79(2), 839–911. https://doi.org/10.3102/0034654308330968
Smale-Jacobse, A. E., Meijer, A., Helms-Lorenz, M., & Maulana, R. (2019). Differentiated instruction in secondary education: A systematic review of research evidence. Frontiers in Psychology, 10, Article 2366. https://doi.org/10.3389/fpsyg.2019.02366
Soland, J. (2019). Modeling academic achievement and self-efficacy as joint developmental processes: Evidence for education, counseling, and policy. Journal of Applied Developmental Psychology, 65, Article 101076. https://doi.org/10.1016/j.appdev.2019.101076
Squire, L. R. (1992). Memory and the hippocampus: A synthesis of findings with rats, monkeys, and humans. Psychological Review, 99, 195–231. https://doi.org/10.1037/0033-295X.99.2.195
Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. Cambridge University Press.
Sternberg, R. J. (1996). Myths, countermyths, and truths about human intelligence. Educational Researcher, 25(2), 11–16. https://doi.org/10.3102%2F0013189X025002011
Sternberg, R. J. (2011). The theory of successful intelligence. In R. J. Sternberg & S. B. Kaufman (Eds.), Cambridge handbook of intelligence (pp. 504–527). Cambridge University Press.
Sternberg, R. J., Grigorenko, E. L., Ferrari, M., & Clinkenbeard, P. (1999). A triarchic analysis of an aptitude-treatment interaction. European Journal of Psychological Assessment, 15, 1–11. https://doi.org/10.1027//1015-5759.15.1.3
Sternberg, R. J., Okagaki, L., & Jackson, A. (1990). Practical intelligence for success in school. Educational Leadership, 48, 35–39.
Suprayogi, M. N., Valcke, M., & Godwin, R. (2017). Teachers and their implementation of differentiated instruction in the classroom. Teaching and Teacher Education, 67, 291–301. https://doi.org/10.1016/j.tate.2017.06.020
Szumski, G., Smogorzewska, J., & Karwowski, M. (2017). Academic achievement of students without special educational needs in inclusive classrooms: A meta-analysis. Educational Research Review, 21, 33–54. https://doi.org/10.1016/j.edurev.2017.02.004
Talbert, E., Hofkens, T., & Wang, M. T. (2019). Does student-centered instruction engage students differently? The moderation effect of student ethnicity. The Journal of Educational Research, 112(3), 327–341. https://doi.org/10.1080/00220671.2018.1519690
Talsma, K., Schüz, B., Schwarzer, R., & Norris, K. (2018). I believe, therefore I achieve (and vice versa): A meta-analytic cross-lagged panel analysis of self-efficacy and academic performance. Learning and Individual Differences, 61, 136–150. https://doi.org/10.1016/j.lindif.2017.11.015
Tomlinson, C. A. (2001). How to differentiate instruction in mixed ability classrooms (2nd ed.). Association for Supervision and Curriculum Development.
Tomlinson, C. A., Brighton, C., Hertberg, H., Callahan, C. M., Moon, T. R., Brimijoin, K., ... & Reynolds, T. (2003). Differentiating instruction in response to student readiness, interest, and learning profile in academically diverse classrooms: A review of literature. Journal for the Education of the Gifted, 27(2–3), 119–145. https://doi.org/10.1177/016235320302700203
United Nations Educational, Scientific and Cultural Organization (UNESCO). (2017). A guide for ensuring inclusion and equity in education. https://unesdoc.unesco.org/ark:/48223/pf0000248254
United Nations Educational, Scientific and Cultural Organization (UNESCO). (2020). Global education monitoring report 2020: Inclusion and education: All means all. https://unesdoc.unesco.org/ark:/48223/pf0000373718
van Geel, M., Keuning, T., Frèrejean, J., Dolmans, D., van Merriënboer, J., & Visscher, A. J. (2019). Capturing the complexity of differentiated instruction. School Effectiveness and School Improvement, 30(1), 51–67. https://doi.org/10.1080/09243453.2018.1539013
Vantieghem, W., Roose, I., Gheyssens, E., Griful-Freixenet, J., Keppens, K., Vanderlinde, R., Struyven, K., & Van Avermaet, P. (2020). Professional vision of inclusive classrooms: A validation of teachers’ reasoning on differentiated instruction and teacher-student interactions. Studies in Educational Evaluation, 67, Article 100912. https://doi.org/10.1016/j.stueduc.2020.100912
Vogt, F., & Rogalla, M. (2009). Developing adaptive teaching competency through coaching. Teaching and Teacher Education, 25, 1051–1060. https://doi.org/10.1016/j.tate.2009.04.002
Vygotsky, L. S. (1978). Mind in society. (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, Eds.). Harvard University Press.
Wang, M. C., & Walberg, H. J. (1983). Adaptive instruction and classroom time. American Educational Research Journal, 20, 601–626. https://doi.org/10.3102%2F00028312020004601
Waxman, H. C., Wang, M. C., Anderson, K. A., & Walberg, H. J. (1985). Adaptive education and student outcomes: A quantitative synthesis. The Journal of Educational Research, 78(4), 228–236. https://doi.org/10.1080/00220671.1985.10885607
Willaby, H. W., Costa, D. S., Burns, B. D., MacCann, C., & Roberts, R. D. (2015). Testing complex models with small sample sizes: A historical overview and empirical demonstration of what Partial Least Squares (PLS) can offer differential psychology. Personality and Individual Differences, 84, 73–78. https://doi.org/10.1016/j.paid.2014.09.008
Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, Article 103599. https://doi.org/10.1016/j.compedu.2019.103599
Xin, Y. P., Chiu, M. M., Tzur, R., Ma, X., Park, J. Y., & Yang, X. (2020). Linking teacher–learner discourse with mathematical reasoning of students with learning disabilities: An exploratory study. Learning Disability Quarterly, 43(1), 43–56. https://doi.org/10.1177/0731948719858707
Yu, C. H., & Osborn-Popp, S. E (2005). Test equating by common items and common subjects: Concepts and applications. Practical Assessment, Research & Evaluation, 10(4), 1–19. https://doi.org/10.7275/68dy-z131