A Proposed Protocol for Measuring the Effectiveness of EUC Education

Learning methods for software such as Excel are still under development. Education designed to keep pace with rapidly evolving software must be updated each time the software changes. There is no final form for such educational methods. Since there is no guarantee that personal computers or operating systems—let alone application software—will retain their current form in the future, even principles that appear universal may eventually be overturned. What matters is building a measurement system capable of adapting to change. To date, instruction in software such as Excel has been delivered by individual educators, each adapting their own approach to address gaps in learning outcomes. Yet the fundamental methodology of software education has not changed in more than thirty years since personal computer training first emerged. In the case of Excel, instruction typically follows a fixed sequence: what Excel is, screen orientation, entering values, entering formulas, copying formulas, and applying formatting. Through this sequence, learners are expected to develop an experiential understanding of Excel. The problem is that instructors cannot judge, in the moment, whether a learner has genuinely grasped what Excel is or has merely learned a sequence of operations. When an instructor asks “Do you understand?” there is no way to tell, without extending the session considerably, whether the learner understood the operation or understood the principle. Given the practical constraints of training delivery, that is not realistic. As a result, learners acquire only procedural knowledge and are unable to handle the novel situations they constantly encounter in the workplace (Sato, 2026). For this reason, end-user computing (EUC) education is in constant need of reform. Whether the current educational framework is sound has gone unmeasured for more than thirty years, even if it may have been assessed at the outset. Prior research on EUC training effectiveness—including Elrod et al. (2015)—has measured skill acquisition through operation-level tests that assess whether learners can replicate demonstrated procedures. However, such tests conflate procedural memory with principled understanding. This protocol addresses that gap by measuring transfer: whether learners can apply an underlying principle to a function they have not been explicitly taught. This paper therefore proposes a methodology for conducting such measurement on a regular basis.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-06-26
DOI
https://doi.org/10.5281/zenodo.20898934
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Proposed Protocol for Measuring the Effectiveness of EUC Education

Yoshihiro SATO
Zenodo (CERN European Organization for Nuclear Research)
Teaching and Learning Programming
article

A Proposed Protocol for Measuring the Effectiveness of EUC Education

Yoshihiro SATO
article en

Abstract

Learning methods for software such as Excel are still under development. Education designed to keep pace with rapidly evolving software must be updated each time the software changes. There is no final form for such educational methods. Since there is no guarantee that personal computers or operating systems—let alone application software—will retain their current form in the future, even principles that appear universal may eventually be overturned. What matters is building a measurement system capable of adapting to change. To date, instruction in software such as Excel has been delivered by individual educators, each adapting their own approach to address gaps in learning outcomes. Yet the fundamental methodology of software education has not changed in more than thirty years since personal computer training first emerged. In the case of Excel, instruction typically follows a fixed sequence: what Excel is, screen orientation, entering values, entering formulas, copying formulas, and applying formatting. Through this sequence, learners are expected to develop an experiential understanding of Excel. The problem is that instructors cannot judge, in the moment, whether a learner has genuinely grasped what Excel is or has merely learned a sequence of operations. When an instructor asks “Do you understand?” there is no way to tell, without extending the session considerably, whether the learner understood the operation or understood the principle. Given the practical constraints of training delivery, that is not realistic. As a result, learners acquire only procedural knowledge and are unable to handle the novel situations they constantly encounter in the workplace (Sato, 2026). For this reason, end-user computing (EUC) education is in constant need of reform. Whether the current educational framework is sound has gone unmeasured for more than thirty years, even if it may have been assessed at the outset. Prior research on EUC training effectiveness—including Elrod et al. (2015)—has measured skill acquisition through operation-level tests that assess whether learners can replicate demonstrated procedures. However, such tests conflate procedural memory with principled understanding. This protocol addresses that gap by measuring transfer: whether learners can apply an underlying principle to a function they have not been explicitly taught. This paper therefore proposes a methodology for conducting such measurement on a regular basis.

Zenodo (CERN European Organization for Nuclear Research)
Arc of the United States (US)
Quality Education
Openalex Percentile: Top 4%
Teaching and Learning Programming
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.