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Hong Kong Teachers’ Self-efficacy and Concerns About STEM Education

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Abstract

STEM (science, technology, engineering, and mathematics) education is vital for incubating future scientists, engineers, and inventors. Teaching and learning in STEM education require teachers and students to employ design thinking and multi-disciplinary knowledge to formulate new solutions for emerging problems. School teachers are facing multiple challenges in implementing STEM education. With the application of the Self-efficacy and stages of concern theories, this quantitative study (with 235 teacher respondents) aims to unearth Hong Kong teachers’ responses regarding STEM education. The results show that 5.53% of the respondents regard themselves as “well prepared” for STEM education. On the other hand, the respondents have intense “information”, “management”, and “consequence” concerns about implementing STEM education in schools. The findings reflect that there is an urgent need to provide teachers with articulated professional development, pedagogic support, and curricular resources for empowering them to implement STEM education in practice.

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Acknowledgements

We would like to thank the participating teachers and our colleagues in CLST who helped the survey conduction.

Author information

Authors and Affiliations

  1. Centre for Learning Sciences and Technologies (CLST), The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, China

    Jie Geng

  2. Department of Curriculum and Instruction and CLST, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, China

    Morris Siu-Yung Jong

  3. Department of Curriculum and Instruction, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, China

    Ching Sing Chai

Authors
  1. Jie Geng
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  2. Morris Siu-Yung Jong
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  3. Ching Sing Chai
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Corresponding author

Correspondence to Jie Geng.

Appendix 1: The brief algorithm of RAKE

Appendix 1: The brief algorithm of RAKE

if test:

text = ”385 teacher concern records”

# Step1: Split text into sentences; a pre-defined utility function to return a list of sentences.

sentenceList = split_sentences(text)

# Identify stop words; load a stop word list “SmartStoplist.txt”.

stoppath = ”SmartStoplist.txt”

stopwordpattern = build_stop_word_regex(stoppath)

# Step2: Generate candidate keywords; split sentences into phrases; a pre-defined utility function to return a list of phrases.

phraseList = generate_candidate_keywords(sentenceList, stopwordpattern)

# Step3: Calculate individual Word scores = deg(w)/frew(w)

wordscores = calculate_word_scores(phraseList)

# Generate candidate keyword scores and return all candidate keywords with the format of (keywords, scores).

keywordcandidates = generate_candidate_keyword_scores(phraseList, wordscores)

# Step4: Select top T-scoring keywords (Mihalcea and Tarau.2004) as final extracted keywords

sortedKeywords

totalKeywords = len(sortedKeywords)

print sortedKeywords[0:(totalKeywords/3)]

# Display results

rake = Rake(“SmartStoplist.txt”)

keywords = rake.run(text)

print keywords

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Geng, J., Jong, M.SY. & Chai, C.S. Hong Kong Teachers’ Self-efficacy and Concerns About STEM Education. Asia-Pacific Edu Res 28, 35–45 (2019). https://doi.org/10.1007/s40299-018-0414-1

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  • DOI: https://doi.org/10.1007/s40299-018-0414-1

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