Evidence map›Paper›PMID 32612561›Full record

ArticleFrontiers in psychology2020

Impact of Learning Methods on Spatial Knowledge Acquisition.

Xiaohe Qiu, Lala Wen, Changxu Wu, Zhen Yang, Qijun Wang, Hongting Li, Duming Wang

Abstract read
In one paragraph

Article in Frontiers in psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Xiaohe QiuDepartment of Psychology, Zhejiang Sci-Tech University, Hangzhou, China.
Lala WenDepartment of Psychology, Zhejiang Sci-Tech University, Hangzhou, China.
Changxu WuDepartment of Systems and Industrial Engineering, College of Engineering, University of Arizona, Tucson, AZ, United States.
Zhen YangDepartment of Psychology, Zhejiang Sci-Tech University, Hangzhou, China.
Qijun WangDepartment of Psychology, Zhejiang Sci-Tech University, Hangzhou, China.
Hongting LiDepartment of Psychology, Zhejiang Sci-Tech University, Hangzhou, China.
Duming WangDepartment of Psychology, Zhejiang Sci-Tech University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research on the acquisition of spatial knowledge not only enriches our understanding of the theory of spatial knowledge representation but also creates practical value for the application of spatial knowledge. The aim of this study is to understand the impact of different learning methods on the acquisition of spatial knowledge, including the role of 2D maps, the difference between physical interaction and virtual interaction, and whether passive learning can replace active learning in virtual environments. One experiment was conducted, in which landmark knowledge and configurational knowledge were measured. Results indicate that 2D maps play a supporting role in acquiring both landmark knowledge and configurational knowledge. In addition, physical learning was associated with better spatial knowledge representation compared with virtual learning. An analysis of observational data in the third comparison found no significant difference between passive learning and active learning using virtual street view maps. However, with high-quality learning materials, passive learning can contribute to the acquisition of spatial knowledge more efficiently than active learning.

Indexed as

2D map-assisted field navigation learningactive learningfield navigation learninglearning methodspassive learningspatial knowledgevirtual street view map learning

Identifiers

PMID32612561
PMCPMC7308434

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.