X-FILEs Jam: Ideation Process and Outcomes

This chapter comprises the motivations behind the X-FILEs Jam, the goals expected, and a thorough description of the day’s events, outcomes, and future recommendations. This student-focused event required teams to respond to a challenge statement that encouraged the creation of a solution to improve or enhance college-level STEM education while incorporating innovative learning environments. Keeping with connective themes of the original 2018 X-FILEs Workshop, the same four technology categories and aspects of teaching and learning were incorporated into the Jam. Each teams’ ideation process was captured and transcribed. The outcomes feature a summary of these students’ contributions leading to their innovative idea-solution. Though the Jam proved to be an effective and educational experience for all, minor adjustments may better align tasks to the larger program goals.

Artificial Intelligence and Machine Learning: An Instructor’s Exoskeleton in the Future of Education

The role of artificial intelligence in US education is expanding. As education moves toward providing customized learning paths, the use of artificial intelligence (AI) and machine learning (ML) algorithms in learning systems increases. This can be viewed as growing metaphorical exoskeletons for instructors, enabling them to provide a higher level of guidance, feedback, and autonomy to learners. In turn, the instructor gains time to sense student needs and support authentic learning experiences that go beyond what AI and ML can provide. Applications of AI-based education technology support learning through automated tutoring, personalizing learning, assessing student knowledge, and automating tasks normally performed by the instructor. This technology raises questions about how it is best used, what data provides evidence of the impact of AI and ML on learning, and future directions in interactive learning systems. Exploration of the use of AI and ML for both co-curricular and independent learnings in content presentation and instruction; interactions, communications, and discussions; learner activities; assessment and evaluation; and co-curricular opportunities provide guidance for future research.

Cross Reality (XR): Challenges and Opportunities Across the Spectrum

Cross Reality (XR) resources hold promise for enhancing instruction and learning experiences in and out of the classroom. Appropriate XR applications can provide the foundation for new types of learning environments and experiences while bringing users together to create unique communities of inquiry and practice. Here we explore the opportunities and benefits of harnessing the affordances of XR while exploring the challenges associated with implementation. Recommendations and implications for future research are also addressed.

Introduction

The practice of educating students in college-level science, technology, engineering, and math (STEM) subjects is influenced by many factors, including education research, governmental and school policies, financial considerations, technology limitations, and acceptance of innovations by faculty and students. Working together, stakeholders in STEM higher education must find creative ways to address the increasing need for a diverse US workforce with a strong STEM background (President’s Council of Advisors on Science and Technology 2012) and the need for a more STEM-literate general population (National Research Council 2012).

Multimodal Learning

The widespread use of technology in the digital age continually shapes how individuals consume knowledge and learn. In the digital age, ideas are shared and represented in multiple formats and through the integration of multiple modes. Technological advances, coupled with considerations of the changing needs of today’s learners, call for exploring new directions for multimodal teaching and learning. Yet, society’s increasing reliance on, and use of, technologies for communication and learning has introduced expanded forms of meaning-making. Information and communication technologies (ICTs) and the online networks that are facilitated by their use encourage educators to transform the way education is delivered. Learning environments are in need of becoming transformed so students are able to use immersive technologies to expand their learning opportunities. This chapter explores emerging trends and pedagogies in multimodal learning that seek to take advantage of the digital tools, texts, and learning approaches that are continually shaping the ways learning occurs inside and outside of higher education.This chapter is outlined to highlight what is found in the literature on multimodal instruction, what findings were realized at eXploring the Future of Innovative Learning Environments (X-FILEs) workshops, and lastly how multimodal instruction can be used to transform the classroom of the future. Throughout this chapter, readers will get to know a student of the future, Juan Delgado. He attends a 4-year university in Dallas, Texas, and is majoring in Mechanical Engineering taking his Introduction to the Fundamentals of Science course. Each aspect of the learning process as it relates to multimodal instruction in 2023 is outlined through the experiences of Juan to situate the impact to learners.

Emergent Guiding Principles for STEM Education

This chapter highlights a series of guiding principles that emerged from the information collected and collated following the X-FILEs Workshop. The guiding principles were developed from themes that appeared in multiple formats and across most or all technology categories. These propositions can inform and serve as the foundation for the design and development of future STEM education. These nine guiding principles, divided into four clusters, emphasize a learning environment that is student-engaged, flexible, and fluid, provides equitable access and accessibility to all, and is personalized to the learner, co-contributed and multiply-sourced, safe and secure, and ethical.

Personalized and Adaptive Learning

Personalized and adaptive learning has been touted to be one of the most promising emerging tools for increasing student learning and student success. Yet, the terms are neither precise nor clearly defined at this time, thus making it difficult for institutions of higher education to adopt and implement a learning approach using technology that is in its infancy and not clearly understood by those who will be utilizing it. One goal of this chapter is to define adaptive and personalized learning as it is used at this time in the hopes that as the technology evolves the promise of increased student learning can come to fruition. Adaptive learning personalizes learning by continuously evaluating each student’s performance in real time and creating an ever-changing individualized learning pathway as directed by artificial intelligence and machine learning, thus increasing learning and student satisfaction.