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Dissertation
Learning Early-Stage Web Development at Scale: Exploring Methods to Assess Learning through Analysis of HTML and CSS
Doctor of Philosophy (Ph.D.), Drexel University
Mar 2021
DOI:
https://doi.org/10.17918/00000559
Abstract
In this dissertation, I argue that web development can play a pivotal role in developing computational thinking as well as provide a stepping stone to an advanced mastery of computation. The preceding literature has extensively examined not only how learners practice computational thinking with a wide array of programming languages, but also how to assess their knowledge and skills against predefined educational criteria. In contrast to the literature on learning traditional programming at scale, there is relatively little known about what people practice and learn in web development, particularly in informal settings at scale. Against this backdrop, I pose the following questions. What knowledge and skills do web developers engage with in their early stages of learning? What mistakes and misconceptions do they make when learning to code in HTML and CSS? How can such learning be measured in informal settings that potentially support a wide range of contexts and purposes? What patterns emerge as people continuously engage in the creation of web artifacts? Guided by these questions, I aim to draw connections between the existing literature and my findings, which is currently missing, in a way attaining a deepened understanding of the skills and concepts that learners practice and know, the errors and misconceptions that they struggle most with, and the creative ways that assess informal learning. Ultimately, my goal is to broaden the notion of computational thinking through web development and to support a broader base of people as computer-literate citizens. To these ends, I conducted four empirical studies situated in a variety of learning contexts, including a laboratory-based study, an educational game, and online web authoring. The contributions of the present dissertation are as follows: (*) A conceptual framework that interprets computational features in web development at multiple levels, binding code features with higher-level features such as abstractions and concepts, which are potentially indicative of learners' knowledge and skills; (*) Data-driven evidence for early-stage web development as a rich, multi-layered activity including a wide range of computational skills and concepts that can broaden notions about computational thinking; (*) An expanded inventory of errors that people make and misconceptions they hold when learning to code in HTML and CSS, and the application of this inventory to large-scale code logs; (*) Design and implementation of learning analytics in informal contexts where collecting data on learning outcomes is challenging or not possible otherwise; and (*) Recommendations on the design of instructional methods and learning tools, as well as future research efforts, filling the gap between theory and practice.
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Details
- Title
- Learning Early-Stage Web Development at Scale
- Creators
- Meen Chul Kim
- Contributors
- Andrea Forte (Advisor)
- Awarding Institution
- Drexel University
- Degree Awarded
- Doctor of Philosophy (Ph.D.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Number of pages
- xiii, 189 pages
- Resource Type
- Dissertation
- Language
- English
- Academic Unit
- Information Science (Informatics); College of Computing and Informatics; Drexel University
- Other Identifier
- 991014952749504721