高校图书馆馆员继续教育刍议

通过对高校图书馆馆员进行继续教育迫切性与必要性的分析 ,为高校图书馆馆员进行继续教育提出合理解决的方法和方式 ,使高校图书馆馆员继续教育得以实现。     

医患交流中患者扩展回答的会话分析研究

面对医生的确认类提问,患者进行扩展回答是医患交流中一种较为常见的现象.尽管扩展回答在交流层面违背提问对回答的结构限定,而且还占用更多的交流时间,但患者的扩展回答并不是一种消极的交流现象.本文采用会话分析的研究方法,以发生在泌尿外科门诊的医患交流为语料,探究患者扩展回答的具体原因及序列语境,分析扩展回答的价值和意义.通过...     

Video demonstrations can predict the intention to use digital learning technologies

The technology acceptance model (TAM) uses perceived usefulness and perceived ease of use to predict the intention to use a technology which is important when deciding to invest in a technology. Its extension for e-learning (the general extended technology acceptance model for e-learning; GETAMEL) adds subjective norm to predict the intention to use. Technology acceptance is typically measured after the technology has been used for at least three months. This study aims to identify whether a minimal amount of exposure to the technology using video demonstrations is sufficient to predict the intention to use it three months later. In two studies—one using TAM and one using GETAMEL—we showed students of different cohorts (94 and 111 participants, respectively) video demonstrations of four digital technologies (classroom response system, classroom chat, e-lectures, mobile virtual reality). We then measured technology acceptance immediately after the demonstration and after three months of technology use. Using partial least squares modelling, we found that perceived usefulness significantly predicted the intention to use three months later. In GETAMEL, perceived usefulness significantly predicted the intention to use for three of the four learning technologies, while subjective norm only predicted the intention to use for mobile virtual reality. We conclude that video demonstrations can provide valuable insight for decision-makers and educators on whether students will use a technology before investing in it.

Practitioner notes

What is already known about this topic

• The technology acceptance model helps decision-makers to determine whether students and teachers will adopt a new technology.
• Technology acceptance is typically measured after users have used the technology for three to twelve months.
• Perceived usefulness is a strong predictor of intention to use the technology.
• The predictive power of perceived ease of use for the intention to use varies from insignificant to strong.

What this paper adds

• For the four digital learning technologies (classroom chat, classroom response system, e-lectures and mobile virtual reality), we measure technology acceptance after a video demonstration and again after three months of usage.
• Using structural equation modelling, we are able to predict intention to use after three months, with perceived usefulness measured after the video demonstration.
• We replicate these findings with a second study using the general extended technology acceptance model.

Implications for practice and/or policy

• Short video demonstrations can provide information for educators to predict whether students will use a technology.
• Early impressions of perceived usefulness are very important and valuable to predict whether students will use a technology.