Teaching without the teacher? Building a learning environment through computer simulations

Proserpio, Luigi; Magni, Massimo

doi:10.1016/j.ijinfomgt.2011.09.002

Cited by 14 publications

(8 citation statements)

References 47 publications

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“…Unlike face-to-face courses, e-learning is completed online and lacks the non-verbal cues of eye contact, movement, and body position (Proserpio & Magni, 2012). This difference drives scholars to explore the e-learning outcomes.…”

Section: Perceived Usefulness and Learning Outcomesmentioning

confidence: 99%

Exploring the factors affecting learners’ continuance intention of MOOCs for online collaborative learning: An extended ECM perspective

Zhou

2017

AJET

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The purpose of this paper was to investigate what factors influence learners' continuance intention in massive open online courses (MOOCs) for online collaborative learning. An extended expectation confirmation model (ECM) was adopted as the theoretical foundation. A total of 435 valid samples were collected in mainland China and structural equation model (SEM) approach was adopted. The descriptive statistics show that platforms from abroad, such as Coursera and Khan, are more popular than native ones in mainland China. The empirical results show that the effects of three ECM factors (satisfaction with prior learning experience, confirmation with prior learning experience, and perceived usefulness) are significant. Different factors have different predicting power. Knowledge outcome is the first powerful indicator of learners' continuance intention of MOOCs, followed by social influence, learners' satisfaction with prior learning experience, and performance proficiency. The effects of knowledge outcome, performance proficiency, and social influence are significant, showing the success of extended ECM.

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Section: Perceived Usefulness and Learning Outcomesmentioning

confidence: 99%

Exploring the factors affecting learners’ continuance intention of MOOCs for online collaborative learning: An extended ECM perspective

Zhou

2017

AJET

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“…From the keyword features and their evolution in this stage, we can see that (1) the concept of technology-supported learning environment is constantly refined and the types are becoming more diverse, such as the integration of AR technology, the widespread use of Second Life games (Huang, 2011) and computer-mediated communication as a mean of more convenient construction of learning environments support (Proserpio & Magni, 2012;Raspopovic et al, 2017);…”

Section: Focus On Virtual Communities To Create Interactive Online Le...mentioning

confidence: 99%

A bibliometric analysis of studies on technology‐supported learning environments: Hot topics and frontier evolution

Jing,

Wang,

Chen

et al. 2024

Computer Assisted Learning

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Background StudyTechnology‐supported learning environments, act as significant observational and enabling indicators for evaluating and encouraging the digital revolution of education, are of vital importance in current educational research. Keeping track of the dynamics of technology‐supported learning environment research allows for the enrichment of theoretical studies and a prompt innovation of talent‐training environments.ObjectivesThe present study carried out a bibliometric analysis of the core collection of WoS database over the last two decades.MethodThis study is a bibliometric research. We applied CiteSpace and VOSviewer for co‐occurrence and evolution analysis. In addition, we extracted the fundamental ideas and thoughts through reading and analysis.ResultsFirst, the study found that technology‐supported learning environment research is in a growth phase, with core journals such as Interactive Learning Environments or Computers & Education. The findings show that a core research team comprised of such scholars as Hwang Gwo‐Jen, Lester James C. and Wong Lung‐Hsiang. Key nations of publishing and research strength are from China, the United States, the United Kingdom and the Netherlands. Second, the hot topics in research on technology‐supported learning environments were virtual learning environments, technology‐enabled learning environments and interactive learning environments. Nine study specifics were derived from these three topics such as moulding function, generating scenario, stimulating sensation, effect verification, design idea, research reflection, inquiry and research, virtual community, and mixed environment. Lastly, the frontier evolution offers a pattern of development from enhancing performance to changing mode to incorporating experience.ConclusionLooking ahead, our research recommendations for the field of technology‐supported learning environments include a multifaceted approach. We should aim to enhance research designs and methodologies, develop contemporary guiding theories and strive for a balanced representation across various educational domains, expanding our scope to encompass all academic disciplines. In terms of practical application, it is imperative to focus on the design and implementation of technology‐supported learning environments from three critical perspectives: “change‐demand,” “student‐learning” and “online‐offline.” These approaches will collectively ensure that technology‐supported learning environments are both innovative and responsive to the diverse needs of the educational landscape.

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“…Unlike in traditional face-to-face courses, teachers in online courses cannot perceive their students' nonverbal cues, such as eye contact, movement, and body posture (Proserpio & Magni, 2012). This difference has encouraged many researchers to pay special attention to exploring online learning outcomes.…”

Section: Learning Outcomes (15/26)mentioning

confidence: 99%

A literature review of questionnaires for the assessment of online learning with a specific focus on the factors and items employed

Zhou

et al. 2021

AJET

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Studies measuring online learning have adopted different perspectives, resulting in different approaches to their assessment of online learning. However, when we consider the literature from a wider angle, there may be complimentary or contrasting relationships. This study performed content analysis on a total of 44 studies that used questionnaires with good reliability and validity from the database of the Social Science Citation Index. Through encoding the 241 factors and 1,171 items used in the questionnaires, we found relationships among them that could be supported by classical theoretical frameworks or our general knowledge of instruction. The content measured by these questionnaires can be divided into five categories according to Li’s (2001) theory of instructional elements: (1) learning environment, (2) learner, (3) teacher, (4) learning content, and (5) learning interaction. Following the initial analysis, this study used the unified theory of acceptance and use of technology (UTAUT) (Oye et al., 2014) to classify 58 factors that reflect the learning environment. In addition, the combination of Marzano and Kendall’s (2007) new taxonomy of educational objectives and learning outcomes provides useful information for 111 factors used to assess learners. Implications for policy and practice: An online learning questionnaire database was established with a good structure based on the classification of the factors and items of these questionnaires. Based on the results, teachers can more accurately design online teaching without feeling lost or taking detours.

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Teaching without the teacher? Building a learning environment through computer simulations

Cited by 14 publications

References 47 publications

Exploring the factors affecting learners’ continuance intention of MOOCs for online collaborative learning: An extended ECM perspective

Exploring the factors affecting learners’ continuance intention of MOOCs for online collaborative learning: An extended ECM perspective

A bibliometric analysis of studies on technology‐supported learning environments: Hot topics and frontier evolution

A literature review of questionnaires for the assessment of online learning with a specific focus on the factors and items employed

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