Inclusiveness in open online computing education: geo-cultural perspectives

开放在线计算教育的包容性：地缘文化视角

基本信息

批准号：
ES/X007243/1
负责人：
Saman Zehra Rizvi
金额：
$ 14.65万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FX007243%2F1
关键词：
Inclusiveness open online computing education

项目摘要

My PhD research set out to study the role of demographics and socioeconomic status in online learning. I sought to contribute to our understanding of how online learners learn from, engage with, and perceive various learning design elements (e.g., instructional videos, reading material, discussion-based activities, and assessments). My doctoral research was conducted in pre-Covid times and focused particularly on massive, open, online courses (MOOCs). Despite the strong expectations of the online learning and teaching community that free and widely advertised MOOCs may potentially address the global disparity in education, most active learners originate from specific developed countries. Prior work suggests that how successful online learners are in achieving their learning goals varies along geo-cultural and socioeconomic dimensions as well as with learning design features. But despite diverse enrolments, most MOOCs adopt a one-size-fits-all design that presents the same set and sequence of learning activities to all learners. My research set out to study how learning designs could be adapted at scale in various contexts to improve learners' persistence. The research benefited from a range of theoretical frameworks (e.g., Extended-GLOBE and Hofstede NCDs, culturally-adaptive user interface designs whose constructs are derived from the Technology Acceptance Model or TAM). For conceptualization of socioeconomics, I used gross national income (GNI) for each country. The analysis methods included decision trees, sequence mining, and cross-validated interactions in survival analysis. The mixed-method research used semi-structured interviews and artefact-mediated questions to investigate the contextual differences in MOOC learners' perceptions about various learning design elements. The analysis combined a qualitative (thematic analysis) method with sentiment mining. My doctoral research clearly demonstrated that in comparison to subgroup/interaction analyses, an overall analysis of online learning data could mask geo-cultural and socioeconomic heterogeneity in the correlations between learning design factors and learner persistence. Consequently, overarching data analysis results primarily reflect the behavioural patterns of the largest subgroup (e.g., Anglo-Saxon geo-cultural group, high-income countries), which can stand in contrast to patterns of other, smaller subgroups (e.g., African or South Asian, lower-middle- and low-income countries). As a result, it can lead to improved outcomes for the majority group while leaving behind members of underrepresented groups. For example, using the voluminous log data from ten large MOOCs, Study 3 in my PhD project examined how the quantified the predictive link between learning design elements (e.g., number of videos, reading material, discussion-based learning activities, and quizzes) and learners' persistence varies across the ten geo-cultural contexts. While the qualitative study, Study 4 used semi-structured interviews to explore learners' perceptions. I identified that cross-cultural learning design preferences may vary across the disciplines (e.g., computing, arts and humanities). This research has already made a valuable contribution in solving part of the jigsaw and outlining new directions for future research. Revisiting my research in a post-Covid world, could not be more timely. The implications and lessons learned from my PhD research will appear more relevant if a replication is performed in a more specific context. Computing technology is the most dominant and diverse MOOC subject area. Therefore, benefitting from the allowance of 25% new research, I propose large-scale survey based mixed-method replication of my studies 3 and 4 in the context of online computing education, more specifically, within the context of Computing MOOC developed by Raspberry Pi foundation, offered via FutureLearn platform. Ethical approvals will be attained well before the start of the fellowship.

我的博士研究开始研究人口统计学和社会经济地位在在线学习中的作用。我试图为我们理解在线学习者如何学习，互动和感知各种学习设计元素（例如，教学视频，阅读材料，基于讨论的活动和评估）。我的博士研究是在预科时期进行的，特别关注大型，开放的在线课程（MOOC）。尽管在线学习和教学社区对MOOC的自由和广泛广泛的MOOC可能有可能解决教育的全球差异，但大多数活跃的学习者都来自特定的发达国家。先前的工作表明，在网上学习者实现学习目标方面的成功如何随着地质文化和社会经济的方面以及学习设计功能而变化。但是，尽管入学率不同，但大多数MOOC都采用了一种适合所有设计的设计，并向所有学习者提供了相同的集合和一系列学习活动。我的研究着手研究如何在各种情况下对学习设计进行大规模调整，以改善学习者的持久性。该研究受益于一系列理论框架（例如，扩展全景和霍夫斯泰德NCD，具有文化适应性的用户界面设计，其结构源自技术接受模型或TAM）。为了概念化社会经济学，我为每个国家使用了国民收入总收入（GNI）。分析方法包括决策树，序列挖掘和生存分析中的交叉验证相互作用。混合方法研究使用了半结构化访谈和人工介导的问题来研究MOOC学习者对各种学习设计元素的看法的上下文差异。该分析将定性（主题分析）方法与情感开采相结合。我的博士研究清楚地表明，与亚组/互动分析相比，在线学习数据的总体分析可以掩盖学习设计因素与学习者持久性之间的相关性中的地理文化和社会经济异质性。因此，总体数据分析结果主要反映了最大的亚组的行为模式（例如，盎格鲁 - 撒克逊地理文化群体，高收入国家），这可能与其他较小的亚组的模式形成鲜明对比（例如，非洲或南方的群体亚洲，中低收入国家）。结果，这可能会导致多数群体的成果改善，同时留下代表性不足的群体的成员。例如，使用来自十个大型MOOC的大量日志数据，我的博士学位项目中的研究3研究了如何量化学习设计元素（例如，视频数量，阅读材料，基于讨论的学习活动和测验）之间的预测联系学习者的毅力在十种地理文化背景下各不相同。在定性研究的同时，研究4使用半结构化访谈来探索学习者的看法。我确定跨文化学习设计偏好可能在整个学科（例如计算，艺术和人文科学）之间有所不同。这项研究已经为解决拼图的一部分做出了宝贵的贡献，并概述了未来研究的新方向。重新审视我在后兴世界中的研究，再也不会及时。如果在更具体的背景下进行复制，从我的博士研究中学到的含义和教训将显得更加相关。计算技术是最主要，最多样化的MOOC主题领域。因此，我从25％的新研究津贴中受益，我建议在在线计算教育的背景下，更具体地说，在Raspberry Pi开发的计算MOOC的背景下，基于我的研究3和4的混合方法复制。基金会，通过FutureLearn平台提供。在奖学金开始之前，将获得道德批准。