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Ji Hyun Yu

Title: Assistant Professor

Department: Learning Technologies

College: College of Information

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, Purdue University, 2013
    Major: Curriculum & Instruction
    Specialization: Learning Design & Technology
    Dissertation: Development and Validation of Pre-service Teachers' Personal Epistemologies of Teaching Scale (PT-PETS)

Current Scheduled Teaching

LTEC 6500.001Introduction to Quantitative Research in Learning TechnologiesFall 2024
LTEC 6500.430Introduction to Quantitative Research in Learning TechnologiesFall 2024
LTEC 5300.002Learning and CognitionFall 8W1 2024
Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Previous Scheduled Teaching

LTEC 6510.001Introduction to Research in Learning TechnologiesSpring 2024 SPOT
LTEC 6500.001Introduction to Quantitative Research in Learning TechnologiesFall 2023 SPOT
Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Published Intellectual Contributions

    Journal Article

  • Dong, L., Hsieh, C., Yu, J. Evolution of engagement and influence in online learning: A social network analysis of an online master's course.
  • Dong, L., Hsieh, C., Yu, J., Tan, Y. Epistemic network analysis of shifting professional perceptions and ethical insights in online learning.
  • Yu, J. Influence of social network dynamics on educational aspirations in MOOCs.
  • Yu, J. Cross-cultural perspectives in online learning: Socioeconomic status perception and its global variations in MOOCs.
  • Yu, J., Chauhan, D., Alqarawy, M., Alqahtani, M. A systematic review of natural language processing applications in personalized learning.
  • Yu, J. Exploring advanced education intentions after MOOC-based certification.
  • Yu, J., Iqbal, R., Johnson, V. Synergizing human expertise with AI in higher education: A systematic review of Human-AI hybrid teaching.
  • Dong, L., Hsieh, C., Yu, J. Uncovering thematic evolution in online education: A topic modeling analysis of professional discourse.
  • Yu, J., Iqbal, R., Johnson, V. Unveiling emerging themes in Human-AI hybrid teaching through Latent Dirichlet Allocation topic modeling analysis.
  • Yu, J., Tang, H. Evolving engagement and motivation in online Learning: A longitudinal study of MOOC participants.
  • Yu, J., Hiltz, B., Yelamar, K., Raval, N. Charting the path to success: Mapping the links between learner engagement behaviors and aspirations in Social Work MOOCs.
  • Yu, J., Hsieh, C., Dong, L. Mapping aspirational networks: Epistemic analysis of Best Possible Self (BPS) themes among instructional design professionals.
  • Karumbaiah, S., Yu, J., Choi, J. Decoding inequities in MOOCs: A Machine Learning approach to understanding learner success and attrition.
  • Yu, J., Perron, B., Yelamar, K., Raval, N. Latent Dirichlet Allocation insights: Student self-reflections and aspirations in Social Work MOOCs.
  • Yu, J., Chauhan, D., Alqarawy, M., Alqahtani, M. Unveiling trends in natural language processing applications for personalized learning: Insights from Latent Dirichlet Allocation analysis.
  • Yu, J. (2023). Learning experience design as collective praxis: Two design cases from higher education.
  • Nawaz, S., Srivastava, N., Yu, J., Baker, R., Khan, A., Kennedy, G., Bailey, J., Baker, R. (2022). How difficult is the task for you? Modeling and analysis of students' task difficulty sequences in a simulation-based POE environment.
  • Watson, W., Watson, S., Fehrman, S., Yu, J., Janakiraman, S. (2020). Examining international students’ attitudinal learning in a higher education course on cultural and language learning.
  • Yu, J., Watson, S. (2020). Identifying subtypes of attitudinal learning among MOOCs learners: A latent profile analysis.
  • Watson, S., Yu, J., Alamri, H., Watson, W. (2020). Preservice teachers’ technology integration attitude change in a course implementing digital badges.
  • Watson, W., Yu, J., Watson, S. (2018). Perceived attitudinal learning in a self-paced versus fixed-schedule MOOC.
  • Watson, S., Watson, W., Yu, J., Caskurlu, S., Janakiraman, S., Fiosk, H. (2018). Attitudinal learning and its relation to gender, age, ethnicity, enrolment purpose, and most impactful learning activity in the science of happiness MOOC.
  • Watson, S., Watson, W., Yu, J., Alamri, H., Mueller, C. (2017). Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed-methods study.
  • Park, Y., Yu, J., Jo, I. (2016). Clustering blended learning courses by online behavior data: A case study in a Korean higher education institute.
  • Capobianco, B., Yu, J., French, B. (2015). Effects of engineering design-based science on elementary school science students’ engineering identity development across gender and grade.
  • Capobianco, B., Yu, J. (2014). Using the construct of care to frame engineering as a caring profession toward promoting young girls' participation.
  • Yu, J., Sun, Y., Strobel, J. (2012). A conceptual K-6 teacher competency model for teaching engineering.
  • Ertmer, P., Newby, T., Yu, J., Liu, W., Tomory, A., Lee, Y., Sendurur, E., Sendurur, P. (2011). Facilitating students’ global perspectives: Collaborating with international partners using Web 2.0 technologies.
  • Ertmer, P., Newby, T., Liu, W., Tomory, A., Yu, J., Lee, Y. (2011). Students’ confidence and perceived value for participating in cross-cultural wiki-based collaborations.
  • Yu, J., Park, B. (2006). Development of the personal learning blog system for supporting self-regulated learning.
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Overall
Summative Rating		 Challenge and
Engagement Index		 Response Rate

out of 5
out of 7		 %
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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