Inquiry-based learning and E-learning: how to serve high and low achievers

Author:

Sotiriou Sofoklis A.,Lazoudis Angelos,Bogner Franz X.ORCID

Abstract

AbstractLarge-scale implementations of effective inquiry-based learning are rare. A European-wide initiative gave teachers access to innovative e-learning tools (ranging from virtual labs, virtual games and simulations to augmented reality applications) for lesson planning and classroom implementation. We examined 668 such implementations across 453 schools within the period of one school year. Teachers could use a platform with digital resources and tools and were encouraged to adopt five different phases of inquiry-based learning: orientation, hypothesizing, planning, analysis, and conclusion. Additionally, an integrated interface for lesson implementation tracked each students’ problem-solving competence (during the inquiry lessons), culminating in about 12,000 datasets. Every user generated an average of 22 digital inquiry-based digital scenarios, each of which required approximately 50.14 min for completion. These scenarios, using high quality resources adapted to school conditions, yielded significant learning outcomes for participating students (age: 14.4 years, gender balanced). While the PISA study identified 10% high achievers on average, we exceeded this number in our framework scoring 20–29% high achievers and 37–42% low achievers (which was close to the 45% PISA average). Offering tools to teachers, which help creating individual inquiry scenarios and monitoring students’ achievement, does not yield any insurmountable obstacles for classroom-implementation of inquiry-based lessons: Compared to the PISA study, levels of high achievers increased even if complex problem-solving competence was required.

Funder

Horizon 2020 Framework Programme

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Education

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Integration of cognitive conflict in generative learning model to enhancing students’ creative thinking skills;Eurasia Journal of Mathematics, Science and Technology Education;2024-09-02

2. Enhancing students' learning outcomes in self‐regulated virtual reality learning environment with learning aid mechanisms;British Journal of Educational Technology;2024-07-31

3. Transdisciplinary Theories and Models for Understanding Learning Outcomes in Higher Education;Advances in Higher Education and Professional Development;2024-07-12

4. An application of the PLS-SEM model for evaluating e-learning user satisfaction during the COVID-19 pandemic;SN Social Sciences;2024-05-27

5. Discovery Space: A Technology-Enhanced Classroom for Deeper Learning in STEM;Reimagining Education - The Role of E-learning, Creativity, and Technology in the Post-pandemic Era [Working Title];2023-10-08

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3