What Can College Teachers Learn From Students' Experiential Narratives in Hybrid Courses?: A Text Mining Method of Longitudinal Data

What Can College Teachers Learn From Students' Experiential Narratives in Hybrid Courses?: A Text Mining Method of Longitudinal Data

Kenneth C. C. Yang (The University of Texas at El Paso, USA) and Yowei Kang (National Taiwan Ocean University, Taiwan)
DOI: 10.4018/978-1-7998-1662-1.ch006

Abstract

Thanks to the rapid development of asynchronous and synchronous instructional technologies (such as Blackboard and Moodle), many college instructors have flipped their classrooms to create a more student-centered learning environment. The emphasis on cultivating students' life-long learning abilities through the enhancement of information literacy or technology-enabled learning has transformed the pedagogical approaches used by many college instructors. This text mining study was based on a corpus of a three-year experiential narrative collected by the instructor from over 15 college-level courses to identify keywords, main topics/themes, and associations of these topical concepts in students' experiential narratives during and after taking these hybrid classes. QDA Miner text mining software was used to analyze these experiential narratives. Results, implications, and limitations were presented.
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Introduction

Technology-Enabled Hybrid Pedagogy

Technology-enabled classroom has recently attracted attention for many educators (Ramey, 2012). Its wide application in both professional and higher educational contexts has made e-learning technologies a global phenomenon (Esnault, 2008). Technologies in the classroom include computers, class websites and blogs, digital microphones, mobile devices, online media, and online study tools (Ramey, 2012). These technologies and their applications have grown exponentially (Dexter, 2017). Web 2.0 technologies and many emerging applications (e.g., chat room, Facebook, tele-conferencing, e-mails, Wi-Fi, mobile devices, etc., to name a few) have increasingly gaining momentum in many traditional and non-traditional classrooms (Buchholz, 2019; Dexter, 2017; Jones & Vollmers, 2008; Murray & Jackson, 2009; Ramey, 2012). For example, the incorporation of these technologies has become widespread among Australian universities (Gosper, McNeili, Woo, Phillips, Preston, & Green, 2011). Similar technologies have been used in the U.S. college classrooms (Jones & Vollmers, 2008). Canada’s OCAD University’s website also claims the administration’s support in creating technology-enabled learning and teaching, ranging from “from hybrid courses that combine face-to-face and online components to fully online courses……through resource sharing, training workshops, and one-on-one consultations” (https://www.ocadu.ca/services/faculty-curriculum-development-centre/e-learning.htm). In the business and professional setting, it is estimated that users of e-learning can increase their productivity over 50%, while this mode of learning will save between 50% and 70% in on-the-job training (Dexter, 2017).

As a growing industry sector, Pappas (2015) reports that, according to a 2013 report by e-learning Industry, in 2011, about $35.6 billion was spent on self-paced E-learning across the globe (Pappas, 2013). In the U.S., e-learning market is estimated to be around USD$190 billion in 2018 and will grow at 7% from 2019 to 2025 (Global Market Insights, 2015). Pappas (2015) observes that around 46% college students have taken at least one online course and it is estimated by 2019, around half of all college classes will be built on a variety of E-learning platforms. e-learning technologies have been extended to a professional setting. It is reported that about 41.7% of Fortune 500 companies use education technologies to train their employees (Pappas, 2015).

According to Global Market Insights (2015), the e-learning market includes three areas of players: technology, providers, and end-user. In terms of the technology players, it includes online e-learning, learning management system (LMS), mobile e-learning, rapid e-learning, virtual classroom, and others (Global Market Insights, 2015). Both service and contents providers have made up of the Provider sector, while the end users include academic, corporate, and government (Global Market Insights, 2015).

Thanks for the rapid developments of these instructional technologies (such as Blackboard or Moodle) and ancillary platforms to facilitate the delivery of contents (such as digital reality technologies and mobile devices) (Buchholz, 2019; Dexter 2017), many college instructors have developed a hybrid and flipped their classroom approach to create a more student-centered environment.

According to NING (n.d.), a hybrid classroom refers to a pedagogical approach “where the students meet face-to-face with the teacher, has extensive Internet content, uses only relevant technology and strives to provide real world context so students leave skilled, knowledgeable and well prepared” (n.p.). The emphasis on cultivating students’ life-long learning abilities through the enhancement of information literacy or technology-enabled learning has transformed the pedagogical approaches used by many college instructors through technology-enabled or-enhanced hybrid pedagogy. Von Heupt (2018) notes that a hybrid classroom is characterized with its technological capacity to integrate traditional face-to-face education with e-learning technologies (such as video conferencing) to allow participants in remote locations to take part in classroom discussion synchronously, to make the best of their learning time through easy access to course materials, knowledge exchange with other students, and networking with their peers.

The introduction of online and hybrid pedagogies is claimed to have the advantages of the face-to-face instruction method because it “combines the best of both worlds, providing opportunities for reaching students with differing learning styles, engaging students in a variety of activities, and presenting both in-person and online content” (Katz, 2008, p. 93). Megan, Bessie, and Anita (2017) empirically examine the effects of course modality changes on student knowledge, skills/performance, and communication apprehension from a public speaking curriculum. Their findings also demonstrate that a hybrid/blended instructional mode improves the quality of instruction, and positive student learning outcomes (as measured by the metrics above) (Megan et al., 2017). However, in spite of the hype about the potential benefits of a hybrid and flipped classroom pedagogy, empirical studies have only shown inconclusive effects on students’ learning outcomes (Keller, Hassell, Webber, & Johnson, 2009). For example, Keller et al. (2009) use a between-subject experiment to compare whether a traditional and a hybrid instructional method will lead to better academic performance (measured by students’ course grade). Their findings conclude that students’ academic performance is not significantly influenced by the modes of instructions, even after controlling SAT scores, performance in its pre-requisite account course, gender, transfer status, and age (Keller et al., 2009).

Key Terms in this Chapter

Online Learning: A term to describe an emerging approach to learn at students’ own premise through advanced information-communication technologies (such as Blackboard, Moodle, YouTube, Virtual Reality) either asynchronously or synchronously.

Learning Technologies: This term refers to a wide range of information and communication technologies (ICTs) that are employed to support learning, teaching, and pedagogical assessment.

Topic Modeling: A popular text mining technique from machine learning and natural language processing study. The term usually refers to a type of statistical model to discover the abstract and latent topics and to identify the hidden semantic structure that occur in media corpus.

WordStat: A term that describes a large dataset that grows in the size of collected data over time. It refers to the size of dataset that surpasses the capturing, storage, management, and analysis of traditional databases. The term refers to the dataset that has large, more varied, and complex structure, accompanies by difficulties of data storage, analysis, and visualization.

E-Learning: The term refers to various methods of technology-enabled learning systems, supported by the use of information-communication technologies (or ICTs). Communication and educational scholars have often claimed that e-learning is capable of transforming how educators teach to improve their pedagogy.

QDA Miner: A commercial text mining software, developed by Provalis Research, to allow researchers to manage, code, and analyze structured or unstructured qualitative data. The package also includes several add-on programs such as WordStat, or SimStat.

Engagement: A popular term to refer to how users may experience with of information-communication technologies (ICTs). This term refers to a psychological state that user experience with an ICT system and application that explains the reason and the result that users want to interact with them to demonstrate a connection to deep and meaningful learning.

Pedagogy: This term refers to a systematic instruction method that is often employed by an instructor to deliver core subject matters and contents to students.

Interactivity: A term originally used in information science, computer science, human-computer interaction, communication, and digital humanity research. The concept of interactivity refers to the capability of the information-communication technologies to enable two-way interaction among participants in a communication process to generate the best effects.

Flipped Classroom: An emerging pedagogical concept and practice to refer to a student-centered classroom where course contents are offered online first to allow students to learn at their own pace and outside the classroom. The instructors allocate the face-to-face classroom time for discussions that help students clarify, practice, and enhance their learning outcomes.

TF (TF-IDF): A term that refers to an acronym of frequency-inverse document frequency. The term refers to a numerical statistic intended to represent how important a word is to a document in a collection of corpus.

Learning Outcomes: A term that describes what students will learn in a class, as demonstrated in terms of measurable improvements in knowledge, skills, and values. The term allows the instructors to offer measurable results to assess educators’ teaching effectiveness and to offer guidelines for future improvement.

Word Cloud Analysis: A popular text mining analytical technique to provide graphical representations of word frequency to highlight relatively important words on the basis of their prominence in the media corpus or source texts.

EdTech: A term that is used to define diverse types of instructional technologies (such as the internet, streaming technologies, cloud storage, digital games, virtual or augmented realities, etc.).

Text Mining: Also, known as text analytics, is a computational method of analyzing and exploring a lot of unstructured or structure textual data by popular text mining software packages (such as QDA Miner, or PolyAnalyst).

Longitudinal Data: A term that is used to refer to a collection of repeated observations of the same group of participants, over a long period of time, to monitor changes in attitudes, beliefs, and behaviors. The term is sometimes called “panel data.”

Learning Experiences: A term that is used to define any traditional or non-traditional educational interactions or other experiences as a result of students’ learning process in either traditional academic or non-traditional (e.g., technology-enhanced) settings inside a classroom or in an outside-of-school locations.

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