Effective teaching and inspiring teaching in different learning environments: Evidence From cluster analysis and structural equation modeling

Yue On James KO, Wanlu LI, Ridwan MAULANA

Research output: Contribution to conferencePapers


Objectives: This study had two aims. The first was to delineate the boundary between effective and inspiring teaching practices with cluster analysis again with a widely-researched instrument, International Comparative Analysis of Learning and Teaching (ICALT) (van de Grift, 2007; 2014) after initial success with Comparative Analysis of Effective and Inspiring Teaching (CETIT), a newly-developed classroom observation instrument that combined features of both effective and inspiring teaching (First Author et al., 2019) (Figure 1). Then, models hypothesizing some theoretical relationships of inspiring teaching behaviors were tested with structural equation modelling (SEM).

Theoretical framework: Behavioral characteristics of inspiring teaching were initially characterized with a small sample by Sammons and colleagues (2014; 2016), but were found somewhat overlapped with those characteristics often associated with effective teaching.

Methods: The hierarchical cluster analysis were conducted with SPSS 25 with five CETIT factors and seven ICALT factors. For SEM performed in LISREL 8.8, only the CETIT factors were used to explore the second aim. An inter-rater reliability over 0.7 (Gwet, 2014) was established using two different training videos before viewing the lesson samples.

Data sources: The lesson sample was based on 260 videotaped lessons from both primary and secondary schools in Hong Kong, Shenzhen, and Guangzhou, selected from a pool of 500+ lessons in an earlier study by First Author and colleagues (2015). Table 1 summarizes the descriptive and correlation results.

Results and conclusions: The hierarchical cluster analysis results showed two clusters identified (Figure 2). Average linkage to estimate the distance among factors was employed to overcome the shortcoming of single and complete linkage (Yim & Ramdeen, 2015). Together with the Agglomeration Coefficients, the results suggested location should stay at stage 10 with an elbow effect (Figure 3), where the factors are clustering into three categories. These results implied a four-factor construction among variables: Collaboration and Flexibility of CETIT plus two separate clusters with mixed variables of CETIT and ICALT. The first cluster of Enthusiasm (CETIT) and three variables of ICALT, Safe and Stimulating Learning Climate, Efficient Organization, Clear and Structured Instructions, Intensive and Activating teaching, and Learner Engagement from ICALT suggests the fundamental generic teaching behaviors that can be found in the majority of teachers. The second cluster consisting of Innovative Teaching and Reflectiveness of CETIT and Adjusting Instructions and Learner Processing to Inter-learner Differences and Teaching Learning Strategies of ICALT suggests more advanced teaching behaviors found in more effective classrooms.
Figure 4 and Figure show two structural equation models with or without Enthusiasm with acceptable fit indices. Although the first model was more parsimonious with better fit indices, it is conceptually less desirable because it suggests that inspiring teaching may occur with the influences of a teacher’s enthusiasm, incompatible with the findings by Sammons et al. (2014; 2016).

Significance: First, cluster analysis indicated two levels of teaching effectiveness and the significance of teaching flexibility. Second, regardless of model parsimoniousness, the SEM findings suggest that teachers can inspire students through their flexibility to adjust teaching strategies and approaches to promote reflections in students cognitively. Copyright © 2020 AERA.


Conference2020 Annual Meeting of American Educational Research Association: "The Power and Possibilities for the Public Good When Researchers and Organizational Stakeholders Collaborate"
Abbreviated titleAERA 2020
Country/TerritoryUnited States
CitySan Francisco, Calif.
Internet address


Ko, J., Li, W., & Maulana, R. (2020, April). Effective teaching and inspiring teaching in different learning environments: Evidence From cluster analysis and structural equation modeling. Paper presented at The American Educational Research Association Annual Meeting (AERA 2020): Symposium session of Teaching quality, teacher-student interactions, and reflections-in-action: Evidence from different educational sectors in China, San Francisco, USA.


Dive into the research topics of 'Effective teaching and inspiring teaching in different learning environments: Evidence From cluster analysis and structural equation modeling'. Together they form a unique fingerprint.