2025 ASEE Annual Conference & Exposition

ABET Student Outcome 7: Using Learning and Study Strategies Inventory (LASSI) Data to Reveal Societal and Generational Effects on Student Learning Skills

Presented at ME Division 8: Measuring What Matters: Concept Inventories, FE Exam, and Learning Skills

Every semester, we get a new group of freshman engineering students. Are the new groups more prepared for learning? Are they getting weaker with time? Or do they bring in the same level of learning skills as their predecessors? This paper offers a tool to answer that question.
Since 2020, the Department of Mechanical Engineering has collected data from the Learning and Study Strategies Inventory (LASSI) to evaluate ABET Student Outcome 7: the ability to acquire and apply new knowledge using appropriate learning strategies.
LASSI evaluates student learning skills across ten scales of Information Processing, Selecting Main Ideas, Test Strategies, Anxiety, Attitude, Motivation, Concentration, Self Testing, Time Management, and Using Academic Resources. These scales provide a detailed picture of students' learning strengths and weaknesses and their evolution with time.
This paper presents a cumulative sum (CUSUM) analysis of freshman and senior LASSI scores for detecting significant shifts in student learning skills over time. The CUSUM approach was selected for its ability to identify persistent changes in the means of the data by plotting the cumulative differences as the mean changes with time.
Freshman CUSUM curves show that out of the ten basis scales of learning, all except two have kept stable means since the Fall of 2020. Information Processing shows a persistent drop; Using Academic Resources shows a persistent rise.
This paper provides the trends of incoming student learning skills over time. The trends also pinpoint the exact time the trends fall outside of stable 2 standard deviation bands, making it possible for future studies to investigate correlation with societal events such as COVID-19 and the introduction of generative artificial intelligence. Further, the paper provides the same trends for graduating seniors and suggests improvement strategies for weakening trends.

Authors
  1. Dr. Anahita Ayasoufi Auburn University [biography]
  2. Dr. Jeffrey C. Suhling Auburn University
  3. Kyle D Schulze Auburn University
  4. Ashu Sharma Auburn University
Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025