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Explorance

AI-Powered Student Voice

Explorance MLY™: Machine learning feedback analytics purpose-built for higher education.

Meet MLY

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.

Deeper Insights with Topic-Specific Categorization and Analysis

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.

Gather More Insights, Wherever Feedback Comes From

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:

Course evaluations

Central and major surveys

External review sites (e.g., Rate My Courses, EDUopinion)

Social media (e.g., Facebook, Reddit)

Go Beyond Insights with Bottom-Up Recommendations and Alerts

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.

Key Highlights

Proprietary Models

Gain access to Student Experience Insights models that evolve and adapt to changing leadership themes, allowing to swiftly understand qualitative data.

Custom Models

Define a personalized category structure and map institution-specific terminology to get a more accurate and comprehensive analysis.

Contextual Categorization

Dig into comments that have been carefully categorized for the student journey, so academic leaders can save time gathering context-specific insights.

Easy Data Processing

Streamline data ingestion in a timely fashion with a solution that can consume and analyze up to 250,000 comments simultaneously.

Insight Discovery

Easily distribute the right data to the right stakeholders ― providing opportunities to identify trends and filter data for granular analysis.

Alerts on Sensitive Matters

Quickly identify comments of concern by automatically flagging inappropriate comments and courses with increased issues before they escalate out of control.

How Explorance MLY Dives into a Single Comment

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:

Sentiment polarity (positive or negative)

Where it belongs (category)

If it includes actionable insight (recommendations)