Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


Siddiqui FINAL 5 2 24 with cert.pdf (1.32 MB)

ETD Abstract Container

Abstract Header

Dr. Lego: AI-Driven Assessment Instrument for Analyzing Block-Based Codes

Siddiqui, Nimra Idris
http://orcid.org/0009-0003-0234-3787
http://rave.ohiolink.edu/etdc/view?acc_num=ysu1714999770360548

Abstract Details

2024, Master of Computing and Information Systems, Youngstown State University, Department of Computer Science and Information Systems.
The field of coding education is rapidly evolving, with emerging technologies playing a pivotal role in transforming traditional learning methodologies. This thesis introduces Dr. Lego, an innovative framework designed to revolutionize the assessment and understanding of block-based coding through the integration of sophisticated deep learning models. Dr. Lego combines cutting-edge technologies such as MobileNetV3 (Howard, 2019), for visual recognition and BERT (Devlin et al., 2018), and XLNet (Yang et al., 2019) for natural language processing to offer a comprehensive approach to evaluating coding proficiency. The research methodology involves the meticulous curation of a diverse dataset comprising projects from the LEGO SPIKE app (LEGO Education, 2022), ensuring that the models are subjected to a broad range of coding scenarios. Leveraging the dynamic educational environment provided by the LEGO SPIKE app (LEGO Education, 2022), Dr. Lego empowers users to design and implement various coding projects, fostering hands-on learning experiences. This thesis delves into methodologies aimed at enhancing coding education by exploring model integration, data generation, and fine-tuning of pre-trained models. Dr. Lego not only evaluates coding proficiency but also provides cohesive and insightful feedback, enhancing the learning experience for users. The adaptability of the framework highlights its potential to shape the future of coding education, paving the way for a new era of interactive and engaging learning experiences.
Abdu Arslanyilmaz, PhD (Advisor)
Feng Yu, PhD (Committee Member)
Carrie Jackson, EdD, BCBA (Committee Member)
30 p.
Computer Science; Engineering; Information Systems; Robotics; Teaching
Machine Learning; Deep Learning; Artificial Intelligence (AI); Neural Network Applications; Educational Technology; Code Block Analysis; Coding Education; Ensemble Learning; Model Integration; Pre-trained Models; Convolutional Neural Networks (CNN); Transformer Models; MobileNetV3; BERT; XLNet; Text-Embedded Image Classification; Lego Spike Program

Recommended Citations

Citations

  • Siddiqui, N. I. (2024). Dr. Lego: AI-Driven Assessment Instrument for Analyzing Block-Based Codes [Master's thesis, Youngstown State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1714999770360548

    APA Style (7th edition)

  • Siddiqui, Nimra. Dr. Lego: AI-Driven Assessment Instrument for Analyzing Block-Based Codes. 2024. Youngstown State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ysu1714999770360548.

    MLA Style (8th edition)

  • Siddiqui, Nimra. "Dr. Lego: AI-Driven Assessment Instrument for Analyzing Block-Based Codes." Master's thesis, Youngstown State University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1714999770360548

    Chicago Manual of Style (17th edition)

ysu1714999770360548
161
© 2024, all rights reserved.
This open access ETD is published by Youngstown State University and OhioLINK.