Automated generation of seven vocabulary activity types with distractor collision prevention

Design · All designs
Evidence · 3 claims (2 for, 1 against) · 1 study (1 causal), q3 · 0 of 1 report an effect size · 3 claims rest on one study

Description

During each training session the algorithm selects target words from the P, S, and L sets and generates activities optimizing predicted learning gains. "At the time of the experiment, the system was capable of generating seven types of activities", including multiple-choice matching in both directions, spelling from an L1 prompt, two listening comprehension tasks, semantic sorting, and fill-in-the-blank sentence completion. A distinctive design feature is collision prevention: "To avoid collisions, WordNet data (Miller, 1995) is used to exclude synonyms and direct hypernyms/hyponyms of the target word from the inventory of possible distractors", while paronyms are given preference to help students distinguish them.

Design Implications

Context

Requirements

• WordNet data and Levenshtein-distance heuristics over glosses are needed for distractor selection; semantic information must be available for a unit to appear in semantics-dependent activity types, with graceful fallback to simpler types otherwise.

Constraints

• The absence of vocabulary production exercises and the limited support for productive use are explained by inherent limitations of automated processing of natural language semantics.

Target Learners

• EFL students working with automatically generated vocabulary activities

Target Goals

• Form-meaning mapping, spelling, listening recognition, and semantic classification of target vocabulary

Claims

• Posttest scores increase monotonically across the tutoring algorithm's acquisition stages, supporting its stage criteria, though control-group items did not differ from items still in active acquisition [+W]
• Lexical units brought to the 'fully learned' stage by the tutor scored significantly lower on the posttest than units students already knew before introduction [-W]
• Supplemental computer-based spaced repetition activities nearly triple long-term vocabulary retention in EFL students compared with conventional instruction alone [+W]

Related Patterns

• Five-phase retrieval practice session crossing question format and level of thinking

Examples

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Key Sources

• Evgeny Chukharev-Hudilainen and Tatiana A. Klepikova. (2016). The effectiveness of computer-based spaced repetition in foreign language vocabulary instruction: a double-blind study. calico journal vol 33.3. https://doi.org/10.1558/cj.v33i3.26055

Page metadata

Field	Value
type	design
id	generated-activity-types-collision-prevention
title	Automated generation of seven vocabulary activity types with distractor collision prevention
description	During each training session the algorithm selects target words from the P, S, and L sets and generates activities optimizing predicted learning gains.
status	review
generated	by process:wiki-ingest on 2026-09-27
trust	unverified — no one has confirmed this page's content
sources	1 — evgeny-chukharev-hudilainen-and-tatiana-a-klepikova-2016

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