Christian Ehninger
Explorance MLY™: Machine learning feedback analytics purpose-built for higher education.
MLY is Explorance AI platform that helps academic leaders gain additional insights into their student body by transforming qualitative feedback into data-driven insights that can be leveraged to support important institutional initiatives and decisions.
MLY provides an in-depth analysis of students’ unstructured communications by determining a comment’s sentiment, identifying feedback patterns, and highlighting when comments are formative forward-looking recommendations.
Specifically trained with student comments, the Explorance machine learning models efficiently categorize feedback, digging deeper into student sentiment towards academic-specific topics. With self-learning algorithms and continuous data training, the solution easily adapts to changing academic themes.
Explorance MLY is a feedback source-agnostic solution that allows you to harness collective intelligence wherever it comes from. It has the capability to integrate into an already existing evaluation process by leveraging its API, or consuming spreadsheets in an online dashboard for ad hoc analysis.
Leverage internal and external data to further understand your student experience, by analyzing feedback from:
Explorance MLY is a purpose-built student feedback analytics solution that highlights recommendations and alerts on sensitive topics. With its ability to analyze qualitative feedback at scale, it effectively provides higher education leaders with timely and actionable recommendations that focus on what an institution should start or stop doing, do more or less of, or change.
Gain access to Student Experience Insights models that evolve and adapt to changing leadership themes, allowing to swiftly understand qualitative data.
Define a personalized category structure and map institution-specific terminology to get a more accurate and comprehensive analysis.
Dig into comments that have been carefully categorized for the student journey, so academic leaders can save time gathering context-specific insights.
Streamline data ingestion in a timely fashion with a solution that can consume and analyze up to 250,000 comments simultaneously.
Easily distribute the right data to the right stakeholders ― providing opportunities to identify trends and filter data for granular analysis.
Quickly identify comments of concern by automatically flagging inappropriate comments and courses with increased issues before they escalate out of control.
When a comment is analyzed, machine learning algorithms scan the content and dissect its different sections. All Explorance MLY Models are trained to identify and highlight the following: