Learning Analytics Specialist

Description

The Office of Teaching, Learning & Technology is seeking a Learning Analytics Specialist to expand our capacity for learning sciences research and learning analytics projects that support student learning.

The Learning Analytics Specialist will support the instructional mission at the University of Iowa by analyzing institutional and student interaction data from learning technologies including generative AI tools to inform teaching practices, develop and evaluate interventions that promote student success, and support evidence-based decision making.

This role will collaborate with campus partners to develop and assess data-informed instructional support services, conduct research on student learning, and communicate findings to faculty, staff, administrators, and other stakeholders. The Learning Analytics Specialist will provide consultation, training, and user support related to learning analytics tools, dashboards, and services, helping stakeholders interpret data and apply insights to improve teaching and learning.

The ideal candidate will have a strong background in data management, statistical analysis, and learning sciences research, with demonstrated experience using R, Python, and machine learning techniques to transform and analyze complex datasets. The successful candidate will also possess strong communication, consultation, and stakeholder engagement skills, with the ability to translate analytical findings into actionable recommendations and support the adoption of data-informed practices across the university.

This position is eligible for hybrid work within the state of Iowa and will require a work arrangement form to be completed upon the start of your employment. Per policy, work arrangements will be reviewed annually and must comply with the remote work program and related policies and employee travel policy when working at a remote location. There is an expectation that this individual be able to travel to Iowa City a minimum of 3-4 times per year.

This position is not eligible for visa sponsorship for employment.

For a full job description, please email Melanie Bell at View email address on click.appcast.io .

About IT Services:

Our mission is to create a welcoming and supportive work environment where everyone feels empowered to be their authentic selves and share ideas. We are committed to cultivating a welcoming and respectful workforce culture where people of all backgrounds and perspectives are empowered to make a positive impact. We embrace this commitment as mission-critical in everything we do and embed it in hiring, incentive, retention, and career-development practices and initiatives.

Qualifications

Required Qualifications:

• Master's degree in statistics or a related field, or an equivalent combination of education and additional related experience.
• Experience (typically 1 year or more) in data analytics projects, including research that incorporates data-informed interventions.
• Proficiency in R for data management, analysis, and visualization.
• Proficiency in SQL.
• Knowledge of various statistical analysis (e.g. descriptive statistics, conductive hypothesis tests, and using multiple linear regression and logistic regression to model outcomes.)
• Excellent written and verbal communication skills.
• Excellent interpersonal skills and demonstrated ability to work productively as part of a team.
• Strong organizational and project management skills, with the ability to coordinate multiple projects and meet deadlines.

Desirable Qualifications:

• Knowledge of machine learning techniques and algorithms.
• Knowledge of Unizin Data Platform and Canvas.
• Knowledge of learning theory and its application in higher education settings.
• Knowledge of University policies, procedures, and regulations.
• Knowledge of methods used in educational data mining.
• Experience with a programming language such as Python.
• Ph.D. in Educational Evaluation, Statistics, or a related field.

Position and Application details:

In order to be considered for an interview, applicants must upload the following documents and mark them as a "Relevant File" to the submission:

• Resume
• Cover Letter

Job openings are posted for a minimum of 7 calendar days and may be removed from posting and filled any time after the original posting period has ended.

Successful candidates will be required to self-disclose any conviction history and will be subject to a criminal background check and credential/education verification. Up to 5 professional references will be requested at a later step in the recruitment process.

For additional questions, please contact Melanie Bell at View email address on click.appcast.io .

There is an expectation that the person in this position will maintain effective, civil, and respectful working relationships with the University of Iowa campus community and uphold a high standard of cultural competency and respect for difference that represents our commitment to a welcoming and respectful work environment.