Evidence has shown that facilitating student-centered learning (SCL) in STEM classrooms enhances student learning and satisfaction [1]–[3]. However, despite increased support from educational and government bodies to incorporate SCL practices [1], minimal changes have been made in undergraduate STEM curriculum [4]. Faculty often teach as they were taught, relying heavily on traditional lecture-based teaching to disseminate knowledge [4]. Though some faculty express the desire to improve their teaching strategies, they feel limited by a lack of time, training, and incentives [4], [5]. To maximize student learning while minimizing instructor effort to change content, courses can be designed to incorporate simpler, less time-consuming SCL strategies that still have a positive impact on student experience.
In this paper, we present one example of utilizing a variety of simple SCL strategies throughout the design and implementation of a 4-week long module. This module focused on introductory tissue engineering concepts and was designed to help students learn foundational knowledge within the field as well as develop critical technical skills. Further, the module sought to develop important professional skills such as problem-solving, teamwork, and communication. During module design and implementation, evidence-based SCL teaching strategies were applied to ensure students developed important knowledge and skills within the short timeframe. Lectures featured discussion-based active learning exercises to encourage student engagement and peer collaboration [6]–[8]. The module was designed using a situated perspective, acknowledging that knowing is inseparable from doing [9], and therefore each week, the material taught in the two lecture sessions was directly applied to that week’s lab to reinforce students’ conceptual knowledge through hands-on activities and experimental outcomes. Additionally, the majority of assignments served as formative assessments to motivate student performance while providing instructors with feedback to identify misconceptions and make real-time module improvements [10]–[12].
Students anonymously responded to pre- and post-module surveys, which focused on topics such as student motivation for enrolling in the module, module expectations, and prior experience. Students were also surveyed for student satisfaction, learning gains, and graduate student teaching team (GSTT) performance. Data suggests a high level of student satisfaction, as most students’ expectations were met, and often exceeded. Students reported developing a deeper understanding of the field of tissue engineering and learning many of the targeted basic lab skills. In addition to hands-on skills, students gained confidence to participate in research and an appreciation for interacting with and learning from peers. Finally, responses with respect to GSTT performance indicated a perceived emphasis on a learner-centered and knowledge/community-centered approaches over assessment-centeredness [13].
Overall, student feedback indicated that SCL teaching strategies can enhance student learning outcomes and experience, even over the short timeframe of this module. Student recommendations for module improvement focused primarily on modifying the lecture content and laboratory component of the module, and not on changing the teaching strategies employed. The success of this module exemplifies how instructors can implement similar strategies to increase student engagement and encourage in-depth discussions without drastically increasing instructor effort to re-format course content.
Introduction.
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Real-World Assignments at Scale to Reinforce the Importance of Algorithms and Complexity
Computer Science students in algorithm courses often drop out and
feel that what they are learning is disconnected from real life programming. Instructors, on the other hand, feel that algorithmic content is
foundational for the long term development of students. The disconnect
seems to stem from students not perceiving the importance of algorithmic
paradigms, and how they impact performance in applications.
We present the point of view that by solving real-world problems
where algorithmic paradigms and complexity matter, students will become more engaged with the course and appreciate its importance. Our
approach relies on a lean educational framework that provides simplified
access to real life datasets and benchmarking features. The assignments
we present are all scaffolded, and easily integrated into most algorithms
courses. Feedback from using some of the assignments in various courses
is presented to argue for the validity of the approach.
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- NSF-PAR ID:
- 10158622
- Date Published:
- Journal Name:
- CCSC NE
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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