Abstract
In this chapter, two cases that include computer science (CS) instructors' integration of an online discussion platform (Piazza) into their courses were examined. More specifically, the instructors' perspectives and role in these cases were explored to gain insight that might enable further improvements. Employing a mixed methods research design, these cases were investigated with text mining and qualitative data analysis techniques with regard to instructors' integration strategies and students' reactions to them. The results of the study showed that among these cases, one entailed a deep integration (Case 1) and the other a shallow one (Case 2). Instructors' presence and guidance through their posting behaviors had a bigger effect than the nature of the course content. Additionally, TA support in online discussions helped address the limitations of the asynchronous discussion when the TAs had the maturity to only respond to questions for which they were adequately prepared.
TopIntroduction
Online discussion platforms are extensively used in all forms of educational environments. This use ranges from being a substantial or primary component of online and blended learning to being a supporting element in face-to-face settings. Online discussion can be used to promote critical thinking, problem solving, and knowledge construction in a collaborative environment (Hewitt & Brett, 2011; Marra, Moore, & Klimczak, 2004; Scardamalia, & Bereiter, 1994) which makes it an excellent tool for socio-constructivist learning (Kent, Laslo, & Rafaeli, 2016). Online discussion has proven learning advantages across a wide range of learning domains including educational psychology (Hara, Bonk & Angeli, 2000), astronomy (Mazzolini & Maddison, 2003), and art (Lin, Hou, Wang, & Chang, 2013).
Researchers have investigated online discussions from different perspectives, such as interactivity (Kent et al., 2016), community of inquiry (Cho & Tobias, 2016), and social behaviors (Cheng, Danescu-Niculescu-Mizil, & Leskovec, 2015) that include social comparison (Vassileva & Sun, 2008) and peer support (Brooks, Panesar, & Greer, 2006). The variety of lenses through which online discussion has been explored is enabled through its capture of rich data that can provide insight into students’ perspectives; Analysis of this data enables the exploration of how students perceive and react to the teaching practices used within a course (e.g., Marra et al., 2004; McKenzie & Murphy, 2000). However, student experiences within online discussions in some fields, such as computer science (CS), still need to be explored in more detail. This study focuses on online discussion integration into CS courses because this field uses online discussion platforms mostly to support question and answer activities (Aritajati & Narayanan, 2013; Brooks et al., 2006) in contrast to many other fields where much of the work investigating forum use focuses on knowledge co-construction in domains where instrumental support is not needed (e.g., Heo, Lim, & Kim, 2010; Wang, Woo, & Zhao, 2009). While other fields also use discussion forum tools to support help-seeking activities in the form of questions and answers, this is the dominant mode of use within CS contexts, and it requires additional study because its usefulness is still questioned (Srba, Savic, Bielikova, Ivanovic, & Pautasso, 2019).
The type of instrumental support that forums are meant to provide in CS education contexts is subject to many of the same “second-order barriers” that are associated with technology integration and factors internal to the instructor (Ertmer, 1999). Additionally, online discussion use within a course can range between deep and shallow integration which is not about instructors’ engagement with the platform but about how deeply the online discussion was integrated into the course processes and activities by the instructor. Consequently, this study explores two instructors’ different strategies to overcome second-order barriers to integrating online discussion in CS courses.
Key Terms in this Chapter
Topic Modelling: Identifying and detecting abstract or latent topics that are not directly observable. Rather, the topics are inferred based on the content of a text and reveal its hidden semantic structures. As such, it produces clusters of similar words that characterize the topic.
Algorithm: A well-defined sequence of instructions for performing a computation in mathematics and computer science.
Learning Analytics: The collection, measurement, analysis, and reporting of data about learners, learning environments, and learning so that it can be acted upon to improve teaching or learning.
Data Structures: Different ways of representing, organizing, and managing data effectively.
Latent Dirichlet Allocation (LDA): A commonly-used topic-modeling algorithm that assumes that a document can be represented as a distribution of topics, and each topic is essentially a collection of phrases with different frequencies.
Latent Topics: Abstract topics that are not directly observable. These topics emerge from a text based on the semantic structures that can be observed within that text. A latent topic is a collection of words.
Term Frequency–Inverse Document Frequency (TF-IDF): A statistic that reveals the relative importance of a word to a document, within a larger collection of documents.
Piazza: An online discussion platform that enables asynchronous discussions for educational purposes.
Text Mining: Automatically extracting information from a text to gain insight into that text.