Inclusiveness in open online computing education: geo-cultural perspectives

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。我试图帮助我们理解在线学习者如何学习、参与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)。我的博士研究是在冰冻前的时代进行的，特别关注大规模的、开放的在线课程(MOOC)。尽管在线学习和教学界强烈期望免费和广泛宣传的MOOC可能会解决全球教育差距，但大多数活跃的学习者来自特定的发达国家。先前的工作表明，在线学习者在实现学习目标方面的成功程度随地理文化和社会经济维度以及学习设计特征的不同而不同。但是，尽管注册人数不同，大多数MOOC采用一刀切的设计，向所有学习者呈现相同的一套和一系列学习活动。我的研究旨在研究如何在不同的环境中大规模调整学习设计，以提高学习者的坚持性。这项研究得益于一系列理论框架(例如，扩展全球和霍夫斯特德非传染性疾病，文化适应的用户界面设计，其结构源自技术接受模型或)。对于社会经济学的概念化，我使用了每个国家的国民总收入(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交互作用。本研究采用半结构化访谈和人造物中介问题，考察了MOOC学习者对不同学习设计要素认知的情境差异。该分析结合了定性(主题分析)方法和情感挖掘。我的博士研究清楚地表明，与分组/交互分析相比，在线学习数据的整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群体(例如盎格鲁-撒克逊地理文化群体、高收入国家)的行为模式，这可能与其他较小群体(例如非洲或南亚、中低收入国家和低收入国家)的模式形成对比。因此，它可以改善多数群体的结果，同时将代表人数不足的群体的成员留在后面。例如，使用来自十个大型MOOC的大量日志数据，我的博士项目中的研究3研究了如何量化学习设计元素(例如，视频、阅读材料、基于讨论的学习活动和测验的数量)与学习者在十个地理文化背景下的持久性之间的预测联系。在定性研究的同时，研究4采用了半结构访谈的方法来探索学习者的认知。我发现，不同学科(如计算、艺术和人文学科)的跨文化学习设计偏好可能有所不同。这项研究在解决部分拼图和勾勒出未来研究的新方向方面已经做出了宝贵的贡献。在后寒流世界中重新审视我的研究，再及时不过了。如果在更具体的背景下进行复制，从我的博士研究中获得的启示和教训将显得更加相关。计算技术是MOOC最主要和最多样化的主题领域。因此，受益于25%的新研究津贴，我建议在在线计算教育的背景下，更具体地说，在由未来学习平台提供的Raspberry Pi基金会开发的计算MOOC的背景下，以大规模调查为基础的混合方法复制我的研究3和4。在团契开始之前很久就会获得伦理上的认可。

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.