---
title: "Development and Evaluation of a Personalized Computer-aided"
description: "Automatic question generation can turn any English text into practice items, but earlier generators… 45 references, cited by 1 paper here."
authors: ["Yi-Ting Huang", "Meng Chang Chen", "Yeali S. Sun"]
year: 2018
venue: "arXiv:1808.09732"
source: "https://arxiv.org/abs/1808.09732"
doi: "10.48550/arxiv.1808.09732"
arxiv: "1808.09732"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/huang-2018-development-evaluation-personalized-computer-aided/"
---
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# Development and Evaluation of a Personalized Computer-aided Question Generation for English Learners to Improve Proficiency and Correct Mistakes

**Authors:** Yi-Ting Huang, Meng Chang Chen and Yeali S. Sun · **Year:** 2018 · **Venue:** arXiv:1808.09732 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1808.09732) · [PDF](https://arxiv.org/pdf/1808.09732) · [DOI 10.48550/arxiv.1808.09732](https://doi.org/10.48550/arxiv.1808.09732) · [arXiv 1808.09732](https://arxiv.org/abs/1808.09732) · [OpenAlex](https://openalex.org/W2889162435)

**Citations:** 5 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 141,840 papers whose bibliographies we read

## Summary
Automatic question generation can turn any English text into practice items, but earlier generators produce many questions with no testing goal and no fit to the individual learner. Questions that are too hard discourage students, and questions that are too easy bore them. Huang, Chen and Sun built a personalized generator of multiple-choice items for learners of English as a foreign language. It covers vocabulary, grammar and reading-comprehension items at several difficulty levels. For each student it chooses which generated items to show, using the student's estimated proficiency and the concepts that the student's earlier wrong answers suggest are still unclear. In a classroom study, students using the personalized quizzes fixed earlier mistakes more often than students who got plain generated questions. They also gained the most between pre-test and post-test and answered harder items correctly. An analysis of the selection strategy found the largest gains when vocabulary items matched the learner's level and grammar and comprehension items sat slightly below it. This matters because it removes the costly human item bank that classic adaptive testing depends on.

## Key points
- The system generates multiple-choice items of three types: vocabulary, grammar and reading comprehension, each at graded difficulty levels.
- Personalization uses two signals:
  - an estimate of the learner's proficiency level;
  - the learner's record of incorrectly answered items, used to find concepts the learner has not mastered.
- The motivation is the cost of Computerized Adaptive Testing (CAT). CAT needs a human-written item bank, and each item must be calibrated on roughly 200–1000 respondents.
- The evaluation compared the personalized generator against non-personalized computer-aided question generation. It measured:
  - how often students corrected earlier mistakes;
  - progress from pre-test to post-test;
  - success on more difficult items.
- The best-performing selection rule set vocabulary items at the student's own level and grammar and reading-comprehension items one level below it.

## Interesting ideas
- Wrong answers are treated as evidence of a specific missing concept, and the next items are chosen to probe that concept again rather than drawn at random.
- The best difficulty offset depends on the skill: lexical items work best at the learner's level, while structural and comprehension items work best a bit easier.
- Items and their difficulty come directly from text, avoiding the large human trial runs that CAT calibration requires.
- In the cited work by Mostow and Jang, distractors are split into ungrammatical, nonsensical and plausible kinds, so the chosen option shows why comprehension failed.
- In the cited MARCT system, trigger words such as measure units are mapped to fixed question templates (a weight unit leads to a weight question).

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): when Mova fails an evaluation item, the failure could be traced to the responsible construction or frame, and later self-play or test batches could be weighted toward that same weakness.
- **I2** (learn): a training curriculum for the parser and readers could use different difficulty margins for lexical versus syntactic or discourse material, instead of one global schedule.
- **I3** (answer): school-course test sets could be generated automatically from the world of a text and tagged with a difficulty estimate, so a reader's progress is measured on fresh items rather than a fixed, costly bank.
- **I4** (learn): negative controls could be built in these three tiers, showing whether an error comes from the tree, the frames or the world model.
- **I5** (back): measures and values in the world layer could select question templates when generating text back from the meaning graph, yielding quantity questions that can be checked.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Yi-Ting Huang, Meng Chang Chen and Yeali S. Sun. 2018. Development and Evaluation of a Personalized Computer-aided Question Generation for English Learners to Improve Proficiency and Correct Mistakes. arXiv:1808.09732.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{huang2018development,
  title = {{Development and Evaluation of a Personalized Computer-aided Question Generation for English Learners to Improve Proficiency and Correct Mistakes}},
  author = {Yi-Ting Huang, Meng Chang Chen and Yeali S. Sun},
  year = {2018},
  doi = {10.48550/arxiv.1808.09732},
  eprint = {1808.09732},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1808.09732}
}
```

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*Updated 24 October 2026*