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1. Evaluating STEM-Based Sustainability Understanding: A Cognitive Mapping Approach

   https://doi.org/10.3390/su13148074  

   Petrun Sayers, Elizabeth L.; Craig, Christopher A.; Skonicki, Emily; Gahlon, Grace; Gilbertz, Susan; Feng, Song (July 2021, Sustainability)

   null (Ed.)

   Management education holds promise for addressing deficiencies in interuniversity science, technology, engineering, and mathematics (STEM), as well as sustainability curricula. Accordingly, we designed, developed, implemented, and longitudinally evaluated interdisciplinary STEM-based curricula in the United States. Students in five sections of business management courses and two sections of STEM courses received a STEM-based sustainability intervention (i.e., an interdisciplinary STEM and sustainability module). To assess student outcomes following the intervention and examine the feasibility of cognitive mapping as a student learning assessment tool, we implemented a pre- and post-course modified cognitive mapping assessment in treatment and comparison courses. To interpret the results, we ran descriptives, correlations, paired sample t tests, and principal component analysis. The t tests suggest that when all coding categories are considered, those participating in curricular interventions listed significantly more sustainability terms. The principal component analysis results demonstrate that treatment courses improved variability explained by 7.23% between pre- and post-tests but declined by 8.22% for comparison courses. Overall, linkages became stronger between parent code categories for treatment courses and weaker for comparison courses. These findings add to existing research related to cognitive mapping and demonstrate the ability of the method to capture changes in student outcomes after exposure to STEM-based sustainability curriculum. 

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2. Immersion and Learning: Cognitive and Affective Outcomes of Immersive Virtual Reality Learning Experiences

   Queiroz, A. C. (May 2020, 70th International Communication Association Conference)

   This study aimed to compare the effects of immersive virtual reality (IVR) videos and 2D educational videos on cognitive (i.e. conceptual knowledge) and non-cognitive (i.e. self-efficacy) learning outcomes. Fifty-three students from an all-girls middle school learned about humans’ impact on the ocean through either IVR videos, using a virtual reality (VR) headset, or through 2D videos, using a computer monitor. Results replicate previous findings suggesting that conceptual knowledge gains between IVR and desktop learning experiences is not significant. Also, results show that participants who watched IVR videos reported higher self-efficacy scores and expressed higher feelings of presence than participants who watched the same videos using a computer monitor. Finally, further analyses revealed that the feeling of presence mediated both cognitive and non-cognitive learning outcomes. 

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3. Sensor-Free Predictive Models of Affect in an Online Learning Environment

   Harrison, A (January 2018, Proceedings of the Eleventh International Conference on Educational Data Mining)

   A significant amount of research has illustrated the impact of student emotional and affective state on learning outcomes. Just as human teachers and tutors often adapt instruction to accommodate changes in student affect, the ability for computer-based systems to similarly become affect-aware, detecting and personalizing instruction in response to student affective state, could significantly improve student learning. Personalized and affective interventions in tutoring systems can be realized through affect-aware learning technologies to deter students from practicing poor learning behaviors in response to negative affective states and to optimize the amount of learning that occurs over time. In this paper, we build off previous work in affect detection within intelligent tutoring systems (ITS) by applying two methodologies to develop sensor-free models of student affect with only data recorded from middle-school students interacting with an ITS. We develop models of four affective states to evaluate and determine significant predictors of affect. Namely, we develop a model which discerns students’ reported interest significantly better than majority class. 

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4. Cost‐Effectiveness of Early Childhood Interventions to Enhance Preschool: Evidence from a Randomized Experiment in Head Start Centers Enrolling Historically Underserved Populations

   https://doi.org/10.1002/pam.22145  

   Knight, David S.; Landry, Susan; Zucker, Tricia A.; Merz, Emily C.; Guttentag, Cathy L.; Taylor, Heather B. (May 2019, Journal of Policy Analysis and Management)

   Abstract We evaluate the cost‐effectiveness of two early childhood interventions that use instructional coaching and parent coaching as levers for improvement. The study design allows us to compare the individual effects of each intervention as well as their combined effect on student outcomes. We find that teachers receiving instructional coaching improve their use of evidence‐based instructional practices, while families receiving parent coaching show increases in numerous responsive parenting behaviors associated with positive child outcomes. Both interventions demonstrate positive impacts on students, but only parent coaching shows statistically significant effects across a range of student outcomes. Instructional coaching alone is substantially less costly and may therefore be the most cost‐effective of the three treatment conditions; however, small sample sizes limit our ability to reach definitive conclusions. Policy simulations suggest that implementing these interventions could raise the overall cost‐effectiveness of Head Start by at least 16 percent. 

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5. Using Thinkalouds to Understand Rule Learning and Cognitive Control Mechanisms within an Intelligent Tutoring System

   Unal, Deniz Sonmez; Arrington, Catherine M.; Solovey, Erin; Walker, Erin. (June 2020, Artificial Intelligence in Education 2020)

   Cognitive control and rule learning are two important mechanisms that explain how goals influence behavior and how knowledge is acquired. These mechanisms are studied heavily in cognitive science literature within highly controlled tasks to understand human cognition. Although they are closely linked to the student behaviors that are often studied within intelligent tutoring systems (ITS), their direct effects on learning have not been explored. Understanding these underlying cognitive mechanisms of beneficial and harmful student behaviors can provide deeper insight into detecting such behaviors and improve predictive models of student learning. In this paper, we present a thinkaloud study where we asked students to narrate their thought processes while solving probability problems in ASSISTments. Students are randomly assigned to one of two conditions that are designed to induce the two modes of cognitive control based on the Dual Mechanisms of Control framework. We also observe how the students go through the phases of rule learning as defined in a rule learning paradigm. We discuss the effects of these different mechanisms on learning, and how the information they provide can be used in student modeling. 

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