The Intersection of Learning Analytics and ABET Accreditation

ABET accreditation serves as a hallmark of quality for engineering and computing programs worldwide, validating that graduates meet rigorous professional standards. The process demands comprehensive evidence that students are achieving defined program outcomes—ranging from technical problem-solving to ethical reasoning and teamwork. Traditional accreditation data collection often relies on manual processes, scattered assessment artifacts, and end-of-course evaluations, which can be time-consuming and prone to gaps.

Learning analytics offers a transformative alternative. By systematically measuring, collecting, and analyzing data about learners and their learning contexts, institutions can generate actionable, data-driven evidence that directly supports ABET’s outcome-based evaluation framework. This approach not only streamlines the accreditation evidence collection process but also enables continuous improvement of curricula, instruction, and student support.

This article provides a practical, in-depth guide to leveraging learning analytics for ABET accreditation—covering the essential data points, implementation strategies, benefits, and common pitfalls. Whether you are an engineering department head, an accreditation coordinator, or a faculty member, you will find actionable insights to strengthen your program’s accreditation posture.

Defining Learning Analytics in the Accreditor’s Context

Learning analytics is the measurement, collection, analysis, and reporting of data about learners and their environments for the purpose of understanding and optimizing learning and the settings in which it occurs. In the ABET context, this means transforming raw student data into meaningful evidence that demonstrates how well students are achieving the program outcomes (SOs) and student outcomes (SOs) defined by ABET’s accreditation criteria.

ABET’s Criteria for Accrediting Engineering Programs (2023–2024) require programs to have “an assessment and evaluation process that periodically documents and demonstrates the degree to which the student outcomes are attained.” Learning analytics provides the infrastructure to automate much of this documentation, moving from sporadic, anecdotal evidence to continuous, systematic evidence collection.

Unlike simple reporting of grades, learning analytics integrates multiple data sources—assessment rubrics, learning management system (LMS) activity, survey responses, e-portfolio submissions, and even co-curricular participation—to paint a holistic picture of student achievement. When aligned with ABET’s outcomes, this data can be used to generate dashboards, reports, and visualizations that accreditation evaluators find compelling and transparent.

Key to this alignment is the mapping of every assessment task to specific student outcomes. For example, a final project in a capstone design course might be directly linked to outcome (c): “an ability to design a system, component, or process to meet desired needs.” Learning analytics tools can collect rubric scores, student reflection texts, and peer evaluations from that project, aggregating them into an outcome portfolio that demonstrates attainment over multiple semesters.

Essential Data Points for ABET Evidence Collection

Effective use of learning analytics for accreditation begins with identifying the data points that provide the most meaningful evidence. The following categories encompass the core data that programs should systematically capture and analyze.

Direct Assessment Data

Direct assessments measure student performance against specific outcomes. These include:

  • Embedded course assessments: Scores on exams, quizzes, labs, and projects that are explicitly mapped to program outcomes. Using a rubric-based scoring approach ensures that each assessment item aligns with the ABET criteria.
  • Capstone and design projects: Final-year projects often integrate multiple outcomes. Analytics can track rubric scores, milestone achievements, and faculty evaluations.
  • Standardized exams and licensure metrics: FE exam pass rates, or results from locally developed exit exams that benchmark outcome achievement.
  • E-portfolio artifacts: Student-submitted work samples tagged by outcome and rated using criterion-referenced rubrics. Platforms like Portfolium or locally built e-portfolios can feed data directly into analytics dashboards.

Indirect Assessment Data

Indirect measures capture perceptions and self-assessments that complement direct evidence:

  • Student surveys: End-of-course surveys, senior exit surveys, and alumni surveys that ask students to self-rate their attainment of outcomes. These can be administered via LMS or tools like Qualtrics.
  • Employer feedback: Surveys of internship supervisors or employers of graduates provide external validation of outcome achievement.
  • Focus group and interview transcripts: Qualitative data from student or faculty discussions that can be coded to outcomes using text analytics.

Engagement and Behavioral Data

While not direct evidence of outcome attainment, engagement metrics can signal areas where students may be struggling, allowing proactive intervention. Key metrics include:

  • LMS activity: Login frequency, content access patterns, assignment submission timeliness, forum participation.
  • Attendance and participation: In-class engagement captured via polling tools or attendance tracking.
  • Time-on-task: Approximate time spent on learning activities, available from some assessment platforms.
  • Social network analysis: Co-authored assignments, peer review participation, and group collaboration patterns that reflect teamwork outcomes.

Demographic and Contextual Data

To ensure equitable outcome attainment, programs should also collect and analyze data disaggregated by student characteristics such as gender, underrepresented minority status, first-generation college attendance, and socioeconomic background. This supports ABET’s emphasis on inclusive excellence and allows programs to identify gaps in achievement across student populations.

Building a Learning Analytics Infrastructure for Accreditation

Implementing a learning analytics framework for ABET requires careful planning, technology selection, and stakeholder buy-in. The following steps outline a practical roadmap.

Step 1: Map Outcomes to Assessment Points

Begin by creating a matrix that links every ABET student outcome to specific courses, assignments, and rubrics. For each outcome, identify at least two to three direct assessment points distributed across the curriculum. This mapping is the foundation for all subsequent data collection and analysis. Use a centralized repository—such as a spreadsheet or a curriculum mapping tool integrated with your LMS—to maintain and version the mapping.

Step 2: Select Data Collection and Analytics Tools

The right technology stack is essential. Many institutions start with their existing learning management system (e.g., Canvas, Blackboard, Moodle) and augment it with specialized platforms. Consider the following categories:

  • Learning Analytics Platforms: Tools like Directus (an open-source headless CMS that can be customized for educational data), Tableau for visualization, or built-in analytics in LMS products. Directus is particularly flexible for creating custom dashboards that pull data from multiple systems—LMS, SIS, survey tools—into a single accreditation evidence hub.
  • Assessment Management Systems: Platforms like AEFIS, Watermark, or liveRubric that directly support outcomes assessment and generate student-outcome-attainment reports.
  • E-Portfolio Systems: Solutions like Portfolium, Mahara, or Folio for collecting and rating artifacts.
  • Survey and Feedback Tools: Qualtrics, Microsoft Forms, or built-in LMS survey modules for collecting indirect assessment data.

Step 3: Establish Data Governance and Privacy Policies

Learning analytics involves handling sensitive student data. Institutions must develop clear policies that address:

  • Transparency: Inform students about which data is being collected, how it will be used, and how they can access their own data.
  • Consent and opt-out options: Provide mechanisms for students to opt out of analytics where possible (e.g., for behavioral tracking not tied to mandatory assessments).
  • Data security: Encrypt data in transit and at rest, limit access to authorized personnel, and comply with FERPA (or equivalent regulations) and any institutional review board requirements.
  • De-identification: For dashboards shared externally with accreditation teams, aggregate and de-identify data to protect individual privacy.

Step 4: Design Analytics Dashboards for Stakeholders

Dashboards should serve different audiences with different levels of granularity:

  • Faculty: Real-time dashboards showing how their students are progressing on outcomes in their courses, with alerts for struggling students.
  • Program coordinators and department heads: Aggregate views of outcome attainment across sections and semesters, trend lines, and comparisons against benchmarks.
  • Accreditation committees: High-level reports that map assessment evidence to each ABET criterion, complete with supporting artifacts and data interpretations.
  • Students: Dashboards that show their own progress on outcomes, identifying strengths and areas for improvement (optional but can enhance student engagement).

Step 5: Integrate Analytics into Continuous Improvement Cycles

ABET accreditation is not a one-time event; it requires ongoing assessment and improvement. Learning analytics should feed into a formal continuous improvement process (often called the “assessment cycle”). Each semester or year, review outcome attainment data, identify gaps, and implement changes—such as revising assignments, updating rubrics, modifying prerequisites, or offering targeted tutoring. Document each change and its rationale, as this evidence of a closed loop is highly valued by ABET evaluators.

Practical Benefits of a Learning Analytics-Enabled Accreditation Process

Institutions that invest in learning analytics for ABET report several significant advantages beyond merely satisfying accreditation requirements.

Data-Driven Decision Making

Instead of relying on opinions or anecdotal feedback, departments can base curriculum decisions on objective evidence. For example, if analytics reveal that only 60% of students are meeting the outcome for “ethical reasoning” in senior design, the faculty can introduce a new ethics module in the junior-level curriculum and track improvement over the following year.

Early Identification of At-Risk Students

Engagement and performance dashboards allow advisors and faculty to spot students who are falling behind on outcome attainment early in the semester. Interventions—such as tutoring, mentoring, or study groups—can be deployed before final assessments, improving student success and decreasing the number of students who fail to meet outcomes.

Streamlined Accreditation Preparation

With a well-designed analytics system, the evidence required for an accreditation self-study report is already organized and up to date. Instead of scrambling to collect artifacts and compile data in the months before a visit, teams can generate reports instantly. This reduces stress, saves time, and leaves more room for analysis and storytelling.

Enhanced Program Benchmarking

Over time, cumulative analytics data enables departments to compare outcome attainment across different course sections, semesters, or even peer institutions (if data sharing agreements exist). This benchmarking can highlight best practices and areas for improvement.

Demonstrating a Culture of Quality

ABET evaluators increasingly value evidence that a program engages in systematic, evidence-based quality assurance. A robust learning analytics program signals to evaluators that the institution takes accreditation seriously and is committed to continuous improvement grounded in data.

Challenges and Considerations When Implementing Learning Analytics

Despite the benefits, institutions face several hurdles in deploying analytics for accreditation. Being aware of these challenges can help you plan effectively.

Data Silos and Integration Complexity

Student data often lives in separate systems—the LMS, the student information system (SIS), the e-portfolio platform, and third-party assessment tools. Integrating these sources into a coherent analytics pipeline can be technically demanding. Using a flexible data platform like Directus or a dedicated education data warehouse can help unify disparate sources, but it requires investment in API development and data mapping.

Faculty Buy-In and Training

Faculty may resist changes to their assessment practices, especially if they perceive analytics as surveillance or an additional administrative burden. Overcoming this requires clear communication about how analytics will support teaching (not evaluate it), along with professional development on creating outcome-aligned rubrics and using dashboard tools. Involving faculty in the design of the analytics system fosters ownership and adoption.

Rubric Quality and Consistency

Learning analytics is only as good as the data it ingests. If rubrics are poorly designed, not aligned to outcomes, or applied inconsistently by instructors, the resulting evidence will be unreliable. Invest in norming sessions where faculty calibrate rubric use, and consider using automated scoring for well-defined criteria to improve consistency.

Overwhelming Data Volume

Without thoughtful design, analytics dashboards can present so many metrics that decision-makers suffer from information overload. Focus on a small set of key performance indicators (KPIs) that directly link to each student outcome, and provide drill-down options for deeper analysis when needed. Use design principles from data storytelling to highlight the most critical findings.

Maintaining Momentum

Accreditation cycles are long (typically six years). It is common for analytics initiatives to lose steam after a successful self-study. To sustain momentum, embed analytics into regular academic governance—such as department meetings, curriculum committee reviews, and annual program reports. Appoint a dedicated analytics coordinator or committee to ensure the system remains active and evolves with changing criteria.

Real-World Examples and Case Studies

Several institutions have successfully integrated learning analytics into their ABET accreditation processes. While specific details vary, common themes emerge.

At a large public engineering school in the Midwest, the senior design capstone course was redesigned to include a digital submission system that automatically tags project deliverables to ABET outcomes (a) through (k). Rubric scores from faculty and industry mentors were fed into a custom dashboard built on a headless CMS similar to Directus. Over four years, the program demonstrated a steady improvement in outcome attainment, which was documented in their accreditation self-study and praised by evaluators.

A smaller private engineering college used an e-portfolio platform and a learning analytics tool to collect artifacts from all required courses. They automated the generation of “outcome attainment reports” that showed percentages of students meeting each outcome, broken down by year, course, and demographic group. The system also flagged when a course’s assessment results were statistically lower than the department average, prompting a curriculum review. During their ABET site visit, the team was able to pull up live dashboards for the evaluators, significantly reducing the time spent answering questions about evidence.

Another notable example comes from a university that used learning analytics to address a specific weakness identified in a previous accreditation cycle: student outcome (d) – “an ability to function on multidisciplinary teams.” By analyzing collaboration data from LMS discussion forums and peer evaluation scores, the program identified that many students were not engaging equally in team projects. They implemented a team training module and restructured project assignments, and the next round of analytics showed marked improvement. This direct linkage between data, action, and improved outcomes formed a powerful narrative in their interim report.

The landscape of learning analytics and accreditation is evolving. Program leaders should watch for these emerging developments.

Competency-Based Education and Microcredentials

As more engineering programs adopt competency-based education (CBE) and offer digital badges or microcredentials, learning analytics will become even more granular. Rather than tracking outcome attainment at the course level, systems will track individual competency mastery across the entire curriculum. ABET has already begun exploring criteria for non-traditional education pathways, and analytics will be essential for providing evidence in these models.

Artificial Intelligence and Predictive Analytics

Machine learning models can predict which students are at risk of not achieving specific outcomes long before the end of a course. These predictive analytics, combined with automated intervention recommendations, will become more common. However, programs must use such tools ethically, avoiding bias and ensuring that predictions are used to support, not penalize, students.

Real-Time Accreditation Dashboards

The next generation of accreditation software will likely offer live, interactive dashboards that evaluators can explore during site visits. This would replace static PDF reports with dynamic data visualizations, supporting more in-depth inquiry and reducing the burden of pre-visit document preparation.

Interoperability Standards

Initiatives such as the IMS Global Learning Consortium’s Caliper Analytics standard and the OneRoster specification are making it easier to exchange data between educational tools. As these standards mature, integrating learning analytics across platforms will become simpler, reducing the technical barrier for smaller institutions.

Getting Started: A Practical Action Plan

If your program is at the beginning of this journey, consider the following short-term steps:

  1. Audit current assessment practices: Identify which courses already have outcome-aligned assignments and rubrics. Determine what data is already being captured (even if manually).
  2. Form a cross-functional team: Include faculty, advisors, IT staff, assessment coordinators, and a senior administrator to champion the initiative.
  3. Select one pilot course or outcome: Instead of trying to transform everything at once, pick a single course or a single outcome to demonstrate the value of analytics. Build a small dashboard and collect feedback.
  4. Invest in a flexible data platform: Evaluate open-source options like Directus or commercial solutions that allow customization to your institution’s data schema. The ability to connect to multiple sources is critical.
  5. Communicate early and often: Share your vision with faculty, students, and advisory boards. Transparency builds trust and reduces resistance.
  6. Plan for continuous improvement: Establish a regular review cycle—quarterly or semester-based—where data is analyzed, actions are discussed, and changes are documented. This rhythm will become the heart of your accreditation process.

By taking these steps, your program can move from manual, stressful accreditation preparation to a streamlined, data-informed practice that not only satisfies ABET requirements but also genuinely improves student learning. Learning analytics is not a magic solution, but when implemented thoughtfully, it becomes a powerful ally in the pursuit of educational excellence and professional accountability.