Cultivating Motivation: The importance of autonomy, competence & relatedness for instruction involving digital archives and objects

What motivates students to learn? More specifically, how can instruction involving primary sources, digital archives, or museum collections become more engaging and meaningful for students? Purdue librarians and instructional developers have found success in addressing issues of motivation by focusing on three aspects of Self-Determination Theory (SDT): autonomy, competence, and relatedness. Autonomy is defined as feelings of volition and choice, students’ ownership of their own learning processes, and endorsement of behaviors requested by an instructor. Competence refers to the degree to which students believe they can perform academically, and relatedness is concerned with student perceptions of feeling connected to other students, the instructor, and the course content. Student perceptions of these three interdependent psychological needs play an important role in fostering or impeding motivation, engagement, and knowledge-transfer. Incorporating principles of SDT in instruction provides specific and measurable goals for fostering student motivation across a variety of instructional contexts. Purdue Librarians gained exposure to the concepts of autonomy, competence, and relatedness in the IMPACT (Instruction Matters: Purdue Academic Course Transformation) program. IMPACT is a program through which faculty redesign foundational courses with the goal of creating learning-centered environments. Librarians, instructional developers, and educational technologists form teams with faculty through a 13-week instructional design process. This panel will apply lessons learned about student motivation to examine how learning experiences which involve digital objects and archives can be structured in ways that motivate students and allow instructors to assess student motivation to influence learning

DINOSAUR FOOTPRINTS FROM THE GLEN ROSE FORMATION (PALUXY RIVER, DINOSAUR VALLEY STATE PARK, SOMERVELL COUNTY, TEXAS)

Dinosaur footprints are found in the Glen Rose Formation and other Lower Cretaceous stratigraphic units over much of central Texas (Pittman, 1989; Rogers, 2002; Farlow et al., 2006). Dinosaur tracks were discovered in the rocky bed of the Paluxy River, near the town of Glen Rose, Texas, early in the 20th Century (Jasinski, 2008; Farlow et al., 2012b). Ellis W. Shuler of Southern Methodist University did pioneering studies on the dinosaur tracks (Shuler 1917, 1935, 1937), and Langston (1974) summarized much of the early literature. What really put the dinosaur footprints of the Paluxy River on the map, though, were the herculean efforts that Roland T. Bird of the American Museum of Natural History made to secure trackway slabs for display at that institution and at the Texas Memorial Museum in Austin (Bird, 1985; Jasinski, 2008). In 1970 Dinosaur Valley State Park was created to protect the dinosaur footprints. This guidebook briefly summarizes earlier work, and also serves as an interim report of research of our group still in progress, concerned with identifying the makers of the Paluxy River footprints, and determining what those animals were up to as they made their tracks. We will offer some comparisons of the dinosaur tracks of the Glen Rose Formation with those from other ichnofaunas around the world. The last quarter-century has seen an explosive increase in the technical literature dealing with dinosaur footprints, and we cannot possibly cite all of the relevant studies. For the sake of brevity we will emphasize publications from the present century, and summary papers and books, as much as possible. Even with this restriction, however, the literature is so vast that the literature-cited “tail” of this report starts to wag the “dog” of the text

Competence and autonomous motivation as motivational predictors of college students’ mathematics achievement: from the perspective of self-determination theory

Abstract Background Applied Calculus courses serve hundreds of thousands of undergraduates as quantitative preparation and gatekeepers across diverse fields of study. The current study investigated how motivational factors are associated with students’ learning outcomes in Applied Calculus courses from the perspective of self-determination theory—a sound comprehensive motivation theory that has been supported by considerable research in psychology and education. In order to have a nuanced understanding of students’ motivation and learning in Applied Calculus courses, we used three different types of learning measures to investigate students’ mathematics achievement, including course grades, a standardized knowledge exam, and students’ perceived knowledge transferability. Results We tested the relationships between motivational factors and learning outcomes with a multi-semester sample of 3226 undergraduates from 188 Applied Calculus classrooms. To increase the precision of our analysis, we controlled for three demographic variables that are suggested to be relevant to mathematics achievement: gender, minority group status, and socioeconomic status. With a series of multilevel modeling analyses, the results reveal that: (1) competence satisfaction predicts college students’ mathematics achievement over and above the satisfaction of needs for autonomy and relatedness; and (2) autonomous motivation is a more powerful predictor of college students’ mathematics achievement than controlled motivation and amotivation. These findings are consistent across different types of learning outcomes. Conclusions Self-determination theory provides an effective framework for understanding college students’ motivation and learning in Applied Calculus courses. This study extends self-determination theory in the field of mathematics education and contributes to the dialogue on advancing undergraduate science, technology, engineering, and mathematics (STEM) education by providing evidence to understand how motivational factors are associated with students’ learning outcomes in undergraduate mathematics courses.http://deepblue.lib.umich.edu/bitstream/2027.42/174021/1/40594_2022_Article_359.pd