Up2B2: Playing English grammar games at the B2 level
Paper · 2017 · lvalue lvalue
Summary
The paper reports on Up2B2, a mobile quiz game built at Universidad Politécnica de Madrid (UPM). The university requires engineering students to reach CEFRL level B2 in English, but most test at A2/B1. Up2B2 belongs to the Mobile Assisted Language Learning (MALL) approach. It offers multiple-choice items on grammar, vocabulary and use of English, wrapped in game mechanics: timed answers, separate competition and training tests, and a public score ranking that shows nicknames instead of real names.
The item bank came from earlier university proficiency exams and had already been statistically validated. Seventy-three students preparing for B2 certification took a pre-test, used the app for four months, and then took a post-test along with a motivation questionnaire.
An ANCOVA comparing matched experimental and control groups showed higher post-test results for app users. Survey analysis pointed to connectivity and interactivity as the main motivational drivers. Usage logs showed students fitting practice into idle moments.
The authors conclude that MALL works as a complement to other teaching resources, not a replacement.
Key points
- The bank holds 500 multiple-choice questions from pen-and-paper exams given over three academic years (six semesters). Each item was validated on more than 2,000 student answers. Each item stores its correct answer, a category and a difficulty level.
- Up2B2 is a multiplatform WebAPP that needs only a browser running JavaScript and CSS, with native Android and iOS versions also available. Questions and game parameters live on the server, so administrators can update them without anyone reinstalling the app.
- Seventy-three students at higher A2 or B1 level used the app for four months between a pre-test and a post-test. Statistically homogeneous experimental and control groups were compared with ANCOVA, and the experimental group scored higher.
- Motivation was measured with a Likert-scale questionnaire covering five factors, analysed with Spearman correlations. Connectivity and interactivity appeared to influence the other factors.
- Usage patterns:
- On a typical day of use, students took one test on 35% of days, 2–10 tests on 55%, and more than 10 on 10%.
- Activity peaked mid-week and around the Spanish lunch hour (13:30), with little use on weekends or Mondays.
Interesting ideas
- I1. Instead of writing new items, the authors reused real exam questions whose behaviour was already known from thousands of past responses, so every item came with trustworthy difficulty data.
- I2. Every question carries metadata on its grammar or vocabulary category and its difficulty, which lets results be broken down by skill rather than reported as one total.
- I3. The app's effect was judged against a matched control group, with ANCOVA adjusting for pre-test scores, so the gain is not just ordinary progress over time.
- I4. Scored competition rounds are kept apart from free training rounds, so practice on a weak area does not distort the ranking.
- I5. Content and game settings live on a server and change in one place for all users at once, instead of being shipped inside every client.
For Mova
How these ideas could serve Mova, our small open language model — its files, stages and tests:
- I1 (answer): School-course test sets could be built from past exam items with known difficulty, so a reader's score can be read against item difficulty rather than as a flat percentage.
- I2 (answer): Tagging each test question by the language phenomenon it probes would show exactly which constructions make Mova's readers fail, which supports explainable error analysis.
- I3 (learn): When a new component or repair rule is added, its effect could be measured against a no-change baseline while controlling for baseline difficulty, in the spirit of negative controls.
- I4 (learn): Self-play could similarly separate exploratory practice runs from frozen, scored evaluation rounds, so that tuning never leaks into reported numbers.
- I5 (learn): Benchmark items and pass thresholds could be kept in one central, versioned store, so every training run and witness parser is evaluated against the same configuration.
Institutions
📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksInformation Systems · 523 worksPsychology · 798 worksDevelopmental and Educational Psychology · 260 works
🧭 Topics 1Research subjects and languages the work is about.English · 1,373 works
Cite
Cite as lvalue lvalue. 2017. Up2B2: Playing English grammar games at the B2 level. Language Value.
@inproceedings{lvalue2017up2b2,
title = {{Up2B2: Playing English grammar games at the B2 level}},
author = {lvalue lvalue},
booktitle = {Language Value},
year = {2017},
doi = {10.6035/languagev.2017.9.6},
url = {https://doi.org/10.6035/languagev.2017.9.6}
}References
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