To equip students for the advanced digital reality that awaits them, infusing computational thinking into their learning habits is vital. This is done through ensuring that technologies are integrated within the learners’ regular practices. This problem begs the question of how to best prepare teachers to achieve optimal or effective teaching performance. This study tries to answer this question by demonstrating how teacher training can be improved and made more effective through the application of distinct learning styles and Myers–Briggs Type Indicator (MBTI) personality types. In this manner, every teacher can receive proper training that addresses their distinct needs and classroom challenges utilizing their preferred personal learning elements. Understanding and accommodating teachers’ unique learning styles (e.g., visual, auditory, kinesthetics, or reading/writing) and MBTI personality types (e.g., ENFP, ISTJ, etc.) can enable teacher preparation programs to tailor their instructional methods for unplugged activities. Using this technique, students will be more interested and motivated in learning, thus they will better understand and retain computational thinking concepts. Teachers’ learning styles are then classified using an LSTM model while teachers’ MBTI personality types are classified based on their input questions using LinearSVC. This model is developed using Python and the Keras library; after training it on a huge dataset of labeled text data that has been pre-processed by removing punctuation marks and converting text to lowercase. Separate pickle files were saved for the tokenizer and label encoder in order to facilitate easy loading. The performance of these classification models is evaluated using standard metrics such as accuracy, precision, recall, and F1-score to ensure robust and effective categorization. The GPT-3.5 language model will be attached to the present model with a view to offering teachers who are under training explanations that best suit their identified learning styles as well as MBTI personality type. For this, GPT-3.5 architecture will be fine-tuned to generate responses that conform exactly to the teacher’s preferences as it gives them profound and reasoned explanations, meeting each teacher’s specific needs regarding individualized instruction and guidance. The GPT-3.5 implementation plan will illustrate how personalized teacher advice can be generated by utilizing such a model. In addition, given a trained machine learning model, it takes the input from a teacher and classifies his/her MBTI personality type along with their learning style at once. Then the GPT-3.5 model generates an in-depth explanation very specific to the teacher’s preferences with its prompt. In conclusion, teacher preparation in regard to embedding computational thinking in lessons with the use of unplugged activities should address the learning styles and MBTI personality types. This will provide a great chance to improve the learning experience of these students. Machine learning and natural language processing technologies embedded into teacher preparation programs promote the personalization of support aimed at engaging individual learners and aspiring educators in entrepreneurship. In this chapter, we illustrate how these thoughts can be implemented and, in particular, describe effective ways of improving the quality of teacher training through the use of computational thinking and unplugged activities.
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