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Observe-Hypothesize-Experiment (OHE) data-driven learning cycle for corpus-based lexical teaching

Design · All designs
Evidence · 1 claim (1 for) · 1 study (1 causal), q1 · 0 of 1 report an effect size · 1 claim rests on one study

Description

The article implements corpus-based lexical instruction as a discovery process in which learners work with instructor-edited language data. Target lexical items are displayed in KWIC format, and learners "observe the language data, hypothesize the lexical use, report their hypothesis to the whole class, and finally find out the correct ways of using the target words." The article grounds this in a learning-by-doing principle and in the contextualized approach to L2 lexical teaching, positioning it as problem-based learning requiring collaboration.

Design Implications

Context

Requirements

  • Instructors edit and select the language data learners are exposed to
  • Teacher training on the corpus-based search engine is provided to students
  • Adequate instruction and teacher facilitation accompany the corpus work

Constraints

  • Direct classroom use of corpora has given rise to many difficulties, per the literature the article cites

Target Learners

  • intermediate-level ESL learners
  • first-year university English majors

Learning Goals

  • L2 vocabulary ability
  • lexical awareness and contextualized word use

Claims

Examples

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Key Sources

  • Jing Shi. (2017). Biting off More Than They Can Chew? The Impact of Pedagogical Application of Corpus on Vocabulary Ability of Intermediate-Level ESL Learners in Mainland China: A Quasi-Experimental Study. English Language Teaching, 10(9). https://doi.org/10.5539/elt.v10n9p232
Page metadata
Field Value
type design
id ohe-data-driven-learning-cycle
title Observe-Hypothesize-Experiment (OHE) data-driven learning cycle for corpus-based lexical teaching
description The article implements corpus-based lexical instruction as a discovery process in which learners work with instructor-edited language data.
status review
generated by process:settle-candidates on 2026-11-01
trust unverified — no one has confirmed this page's content
sources 1 — jing-shi-2017

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