Learning anytime, anywhere: a spatio-temporal analysis for online learning

被引:21
|
作者
Du, Xu [1 ]
Zhang, Mingyan [1 ]
Shelton, Brett E. [2 ]
Hung, Jui-Long [2 ]
机构
[1] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China
[2] Boise State Univ, Dept Educ Technol, Boise, ID 83725 USA
基金
美国国家科学基金会;
关键词
Online course; spatio-temporal analysis; anytime anywhere; learning performance; PERFORMANCE; ANALYTICS; STUDENTS; ENTROPY; PROGRAM;
D O I
10.1080/10494820.2019.1633546
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
The study proposes two new measures, time and location entropy, to depict students' physical spatio-temporal contexts when engaged in an online course. As anytime, anywhere access has been touted as one of the most attractive features of online learning, the question remains as to the success of students when engaging in online courses through disparate locations and points-in-time. The procedures describe an analysis of 5293 students' spatio-temporal patterns using metadata relating to place and time of access. Grouping into segments that describe their patterns of engagement, results indicate that the high location-high time entropy (i.e. multiple times, multiple locations) was the segment with lowest success when compared with other students. Statistical and modeling results also found that female students tended to learn at fixed or few locations resulting in the highest performance scores on the final exam. The primary implication is that female students tend to be successful because they study in fewer locations, and all students who study at consistent times outperform those with more varied time patterns. Existing brain research supports the findings on gender differences in learning performance and spatio-temporal characteristics.
引用
收藏
页码:34 / 48
页数:15
相关论文
共 50 条
  • [41] Beyond Cross-Section: Spatio-Temporal Reliability Analysis
    Santini, Thiago
    Rech, Paolo
    Nazar, Gabriel Luca
    Wagner, Flavio Rech
    ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, 2016, 15 (01)
  • [42] Spatio-Temporal Analysis of LTP-like Neuroplasticity in Pigs
    Danyar, Mikkel Bjerre
    Clark, Hjalte Faerregard
    Atchuthan, Nickolaj Ajay
    Daugbjerg, Lasse Krogh
    Andersen, Amalie Koch
    Janjua, Taha Al Muhammadee
    Jensen, Winnie
    2023 11TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING, NER, 2023,
  • [43] Spatio-temporal analysis of pneumonia and influenza hospitalizations in Ontario, Canada
    Crighton, Eric J.
    Elliott, Susan J.
    Kanaroglou, Pavlos
    Moineddin, Rahim
    Upshur, Ross E. G.
    GEOSPATIAL HEALTH, 2008, 2 (02) : 191 - 202
  • [44] Traffic Accident Prediction Based on Deep Spatio-temporal Analysis
    Yu, Le
    Du, Bowen
    Hu, Xiao
    Sun, Leilei
    Lv, Weifeng
    Huang, Runhe
    2019 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI 2019), 2019, : 995 - 1002
  • [45] PyVT: A toolkit for preprocessing and analysis of vessel spatio-temporal trajectories
    Li, Ye
    Ren, Hongxiang
    Li, Haijiang
    SOFTWAREX, 2023, 21
  • [46] Spatio-Temporal Analysis of Brand Interest using Social Networks
    Lopes-Teixeira, Diana
    Batista, Fernando
    Ribeiro, Ricardo
    2018 13TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI), 2018,
  • [47] SPATIO-TEMPORAL ANALYSIS OF THE CHANGING ELECTORAL CULTURE OF WOMEN IN BIHAR
    Bharti, Prerna
    Ghose, Debjani Sarkar
    GEOGRAPHIA-UFF, 2025, 27 (58):
  • [48] Terrorism in Egypt: a comprehensive spatial, spatio-temporal, and statistical analysis
    Younes, Ali
    Mohamadi, Bahaa
    Abu Ghazala, Mohamed O.
    GEOJOURNAL, 2023, 88 (06) : 6339 - 6364
  • [49] A Probabilistic Framework for Temporal Cognitive Diagnosis in Online Learning Systems
    Liu, Jia-Yu
    Wang, Fei
    Ma, Hai-Ping
    Huang, Zhen-Ya
    Liu, Qi
    Chen, En-Hong
    Su, Yu
    JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2023, 38 (06) : 1203 - 1222
  • [50] Machine Learning Based Estimation of Ozone Using Spatio-Temporal Data from Air Quality Monitoring Stations
    Chiwewe, Tapiwa M.
    Ditsela, Jeofrey
    2016 IEEE 14TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), 2016, : 58 - 63