STEM Education

This week, I have enjoyed several conversations with colleagues on STEM teaching. So, I would like to share a recent article entitled, “The exploration of continuous learning intention in STEAM education through attitude, motivation, and cognitive load” by Wu et al. (2022). This study uses a learning cycle and a comprehensive research framework that integrates Bloom’s taxonomy: cognitive load, attitude and motivation and psychomotor domain to explore the relationship between learning and intention. The model includes second-order factors derived from the technology acceptance model (TAM) (perceived usefulness, perceived ease of use, and perceived enjoyment), the attention, relevance, confidence and satisfaction (ARCS) model, and cognitive load to explain the continuous learning intention of STEAM education.

The empirical experiment was conducted at a university and an elementary school; a total of 145 data were collected. The results showed that perceived usability directly influenced learning intention and strengthened the relationship between learning attitudes and intention. The ARCS plays a critical moderating role that positively influences perceived usability and strengthens its effects on learning attitudes. Regarding the mediating effects, cognitive load negatively influenced perceived usability.

The study proposed the following hypotheses:

1. Perceived usability has a positive and significant effect on attitude;

2. Attitude has a positive and significant effect on intention;

3. Perceived usability has a positive and significant effect on intention;

4. Cognitive load has a negative and significant effect on perceived usability;

5. ARCS has a positive and significant effect on attitude;

6. Perceived usability positively moderates the relationship between attitude and intention;

7. ARCS positively moderates the relationship between perceived usability and attitude.

The research results support attention, relevance, confidence and satisfaction learning, which reflects cross-domain and hands-on learning, life application, problem solving, and sense learning, and applied such concepts to an AI-based task (Li et al., 2018).

In addition to this research, I will share the article ”the application of AI technologies in STEM education: a systematic review from 2011 to 2021” where the authors conducted a systematic review to examine 63 empirical AI-STEM research from 2011 to 2021, grounded upon a general system theory (GST) framework. Automated AI technologies, e.g., intelligence tutoring, automated assessment, data mining and learning analytics, have been used in STEM education (Chen et al., 2020). Six types of AI applications were identified in order of frequency, learning prediction, intelligent tutoring system, student behavior detection, automation and educational robots.

Regardless of your academic area, I am hopeful that there are pedagogical/andragogical aspects of these approaches to teaching and learning, which could be an asset to your continuous instructional enhancements.

References

Wu, C., Liu, C. & Huang, Y. M. (2022) The exploration of continuous learning intention in STEAM education through attitude, motivation, and cognitive load. IJ STEM Ed, 9(35). https://doi.org/10.1186/s40594-022-00346-y

Xu, W. & Ouyang, F. (2022). The application of AI technologies in STEM education: a systematic review from 2011 to 2021. IJ STEM Ed, 9(59). https://doi.org/10.1186/s40594-022-00377-5