Measuring the Digital Transformation of Education and Teaching

Measuring the Digital Transformation of Education and Teaching

Christian Glahn (Leiden-Delft-Erasmus Center for Education and Learning, Switzerland)
DOI: 10.4018/978-1-5225-8476-6.ch019


The digital transformation has reached higher education and many faculty members find teaching in the digital environment hard. A key question for educational institutions is whether the uptake of blended learning within their digitization strategies matches the pace of technological innovation. This chapter discusses a model for monitoring the progress of educational digitization that has been in use throughout four years at HTW Chur, Switzerland. The model connects technologies to practices rather than abstracting technologies from them. This helps identifying performance indicators in campus-wide information systems for understanding the diffusion of technology uses among the faculty, and it helps categorizing new technologies towards their organizational innovation potential. The combined use of these performance indicators with the model supports tailoring faculty development activities for digitization strategies that are based on the actual development needs within the institution.
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This chapter is based on a journey of digitalizing higher education programs from traditional face-to-face teaching to blended learning at the level of an entire university. It is a response to the uphill struggle between expectations, assumptions, scale, and impact on the one hand, and organizational culture, practices, technologies, and budgets on the other. In this process, the role of faculty development is to prepare and empower the professors and lecturers for the efficient use of digital technologies and infrastructures for teaching. Conventional approaches to these challenges are training sessions, information events, competitions on innovative teaching, communities of practice or communities of inquiry (Garrison & Vaughan, 2008). However, these approaches require a good knowledge of the development needs, are hard to scale with the pace of technological innovation, are resource intensive, or have a limited impact on the organization as a whole.

The present work is rooted in combined investments in digital technologies and faculty development in relation to changing organizational practices from traditional university teaching to blended learning. Development interventions are a crucial success factor for this process because the transformation from primarily analog to digitally incorporated teaching practice also changes the faculty’s development needs. Aiming these interventions too low, creates the impression among the faculty that they surpass the expectations of the transformative process, while aiming too high, creates the impression that the actual development needs are not respected. Both cases lead to tensions and to frustration in the organization that can endanger the transformative process and challenge faculty development as an organizational change agent if the organization’s uptake of new practices is tied to ambitious timeframes and implementation plans. This raises questions on the nature and speed of transitions from traditional teaching to blended learning.

Responding to such questions requires faculty development units to be aware of the dynamics within their organizations while digitalizing education. This turns out to be a difficult problem because many activities are not communicated or are hidden, because the activity has been shifted into the digital realm.

The present research started from the fundamental question about the changing practices during transformation projects: What data accounts for the organizational adoption of digital technologies and blended learning?

Answers to this question help to choose appropriate supportive strategies for the different groups among the faculty. Such answers are also crucial for managing the complexity of digital transformation projects. This chapter addresses this question by presenting a meta-framework for planning and monitoring the dynamics and progress of faculty development interventions during the digital transformation of universities, in which blended learning practices are no longer considered as educational innovation but as non-optional elements of the teaching practice.

The framework presented in this chapter strives to structure the transformative process, to communicate progress, and to support the in-situ identification of barriers and bottlenecks as well as to guide the decisions to overcome them. Such transformations are closely related to the adoption of innovation (Rogers, 2003). Therefore, this chapter analyses the driving factors of the adoption of innovation that influence the cascades of smaller adoption processes that structure the overall transformation. From the viewpoint of faculty development, each adoption process is related to the mastering of different competencies, which in turn define the development needs. Moreover, adoption models represent the dynamics of the faculty’s uptake of new practices by predicting the quantities within a cohort and a timeframe. This makes these models particularly useful for faculty development as these models help to identify competence gaps, barriers, bottlenecks, or diversions during the transformation process as well as provide performance and cost indicators for strategic decisions.

Key Terms in this Chapter

Educational Design: The process of planning and preparing educational processes. Educational designs determine the experiences of learners.

Asset Specificity: The number of product or concept specific factors that refers to the unique features of products, ideas, or practices that differentiate them from other solutions. Asset specificity contributes to uncertainty factors that hinder potential adoption.

IMS LTI: Learning tools interoperability (LTI) is a standard created by the IMS Global Learning Consortium that links content and resources to learning platforms.

Digital Transformation: The reorganization of organizational processes towards the optimal usage of digital technologies and resources.

Adoption: The integration of a concept, idea, product, or service into an existing practice.

Transaction Costs: The “overhead” costs that contribute to pricing and influence adoption decisions. Transaction costs include necessary investments in developing new procedures and practices, competence development and training, as well as infrastructure.

Orchestrated Learning Activities: Sequences or arrangements of learning activities that allow lecturers and students to monitor the learning progress. Lecturers may decide to change the arrangement and flow of their educational design based on this information.

Basic Learning Activities: The parts of educational processes that require student activity that are not related to other learning activities other than by their co-location.

Blended Learning: An educational design principle that relies on connecting face-to-face and online learning experiences. Typically, onsite courses become blended when online activities are designed to replace some onsite sessions.

Data-Driven Orchestration: Automated orchestration of learning activities by using performance data of individual students or groups of students. The automated orchestration is based on conditions and completion rules that are defined in an educational design.

Course Organization: The administrative tasks that are part of educational processes. These tasks include student information, organization of group work, scheduling of activities, and collecting assignments for grading.

Adoption Rate: The number of adoptions by market actors or organization members at a given time.

Learning Resource Management: The organization and arrangement of learning resources, including text documents, images, infographics, HTML text, videos, audio files, and links to internet resources.

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