乐胖代购免代理版

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One typical application of AI in digital language education is the automatic scoring application that provides feedback on pronunciation repeat outcomes. This research is motivated by the usage of automatic scoring\u2010empowered digital learning tools by language learners, and set out to uncover the influencing mechanisms of AI\u2010enabled automatic scoring application affordances on learners\u2019 continuous learning intention. Specifically, based on affordance theory, we found several automatic scoring application affordances through in\u2010depth interviews. Considering the current lack of investigations on the mechanisms underlying automatic scoring application and its implications for learners\u2019 learning behaviors, we built a model to examine the role of automatic scoring application affordances on cognitive\/emotional engagement and following continuous learning intention. We further examined the moderation role of in\u2010job learners and student learners on the above relationships. The model was tested using a survey of 260 Chinese foreign language learners who used AI\u2010empowered learning tools to facilitate their language learning practices. This study explores why learners continuously use AI\u2010enabled automatic scoring applications by identifying the affordances that differentiate it from traditional educational technologies. Practitioners could take the identified affordances into account when designing AI\u2010enabled language learning applications.<\/jats:p>","DOI":"10.1111\/bjet.12995","type":"journal-article","created":{"date-parts":[[2020,7,15]],"date-time":"2020-07-15T06:59:50Z","timestamp":1594796390000},"page":"1674-1692","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["The affordances of AI\u2010enabled automatic scoring applications on learners\u2019 continuous learning intention: An empirical study in China"],"prefix":"10.1111","volume":"51","author":[{"given":"Shixuan","family":"Fu","sequence":"first","affiliation":[]},{"given":"Huimin","family":"Gu","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6772-7402","authenticated-orcid":false,"given":"Bo","family":"Yang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2020,7,14]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/bjet.12354"},{"issue":"20","key":"e_1_2_9_3_1","first-page":"1","article-title":"Virtual learning during the COVID\u201019 pandemic: A disruptive technology in graduate medical education","volume":"75","author":"Almarzooq Z.","year":"2020","journal-title":"Journal of the American College of Cardiology"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1037\/0022-3514.51.6.1173"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781118619179"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2011.07.029"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1037\/0022-3514.52.1.47"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.14687\/ijhs.v13i1.3549"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2018.08.016"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2018.08.048"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/isj.12265"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2013.02.018"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2575.2007.00238.x"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2014.05.005"},{"issue":"2","key":"e_1_2_9_15_1","first-page":"1","article-title":"Incorporating learning characteristics into automatic essay scoring models: What individual differences and linguistic features tell us about writing quality","volume":"8","author":"Crossley S. 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