当前位置: X-MOL 学术Computer Science Education › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
How prior experience and self-efficacy shape graduate student perceptions of an online learning environment in computing
Computer Science Education ( IF 3.0 ) Pub Date : 2019-04-13 , DOI: 10.1080/08993408.2019.1601459
Quintin Kreth 1 , Mary Eve Spirou 2 , Sarabeth Budenstein 3 , Julia Melkers 1
Affiliation  

ABSTRACT Background & Context: As graduate programs in computing expand to online environments, existing research is limited in its ability to inform both practical and theoretical understandings of the factors influencing student success. Objective: We explore student perceptions of their learning environment at the understudied intersection of online graduate education, STEM, and adult learning. We focus on how students’ human capital influences self-efficacy and how self-efficacy shapes perceptions of the learning environment. Method: We conduct a survey of students in a large, online MS program in computer science, then examine those findings using descriptive statistics and OLS. Findings: Findings indicate gender variation in both learning and computing self-efficacy and in how positively students perceive the online learning environment. Students with prior online education experiences have lower learning self-efficacy. Implications: Low learning self-efficacy is one potential mechanism for why online students struggle to succeed, even in specialized fields. Adapting support structures, advising, and admissions may help address these challenges.

中文翻译:

先前的经验和自我效能如何塑造研究生对计算在线学习环境的看法

摘要背景与背景:随着计算研究生课程扩展到在线环境,现有研究在为影响学生成功的因素提供实践和理论理解方面的能力有限。目标:我们在未充分研究的在线研究生教育、STEM 和成人学习的交叉点上探索学生对其学习环境的看法。我们关注学生的人力资本如何影响自我效能以及自我效能如何塑造对学习环境的看法。方法:我们在计算机科学领域的大型在线 MS 项目中对学生进行调查,然后使用描述性统计和 OLS 来检查这些发现。调查结果:调查结果表明学习和计算自我效能感以及学生对在线学习环境的积极看法存在性别差异。有过在线教育经验的学生学习自我效能感较低。启示:低学习自我效能感是在线学生即使在专业领域也难以成功的潜在机制。调整支持结构、建议和录取可能有助于应对这些挑战。
更新日期:2019-04-13
down
wechat
bug