Student Responses to Active Learning Strategies: A Comparison Between Project-Based and Traditional Engineering Programs

Prior research has shown that active learning strategies in engineering education improve student learning, motivation, and retention in STEM disciplines. Yet, instructors are often hesitant to use active learning and other non-lecture strategies due to challenges from students who are resistant to engaging in these methods. Prior research has suggested strategies that can be used to mitigate student resistance to active learning, yet many faculty members have not yet implemented active learning into their engineering education courses. The global demand for entrepreneurially-minded engineers and the rapid growth of engineering programs embracing this mindset increases the need for actionable resources and strategies for faculty to implement in their courses. The program reported here uses active learning across the curriculum, encounters little student resistance, and graduates industry-ready engineers. We report the findings from the Student Resistance to Instructional Practices (StRIP) study focused on students in a specific project-based learning engineering education program, and compare results to non-PBL previous studies. The results indicated that PBL engineering students enjoyed the active learning strategies used by their instructors, and showed less resistance to them. The PBL learners reported less frequent use of non-lecture activities in courses than previous studies. Possible reasons for this result are presented

Applying Design Based Research to New Work-Integrated PBL Model (The Iron Range Engineering Bell Program)

A new project-based model of engineering education is being developed to deliver an upper-division (final two years of four-year bachelor degree) experience. The experience is centred on students working directly in industry through engineering apprentice (cooperative education/internship) employment. Students will work in industry, completing projects, for the last two years of their education while being supported in their technical and professional development by professors, facilitators, and their peers through use of digital communication. This new model focuses on learning being more imbedded in professional practice, in contrast to the more traditional model of engineering, where the learning about the profession is done in the abstract of a classroom. The learning experience is designed to open doors for greater access to engineering education. Developed for community college graduates (entering students who have completed first two years of engineering bachelor requirement) in the United States, the program will serve a more ethnically and gender diverse student body. The innovative new model focuses on the development of transversal competences, a new set of teacher roles in PBL, industry-university collaboration, curricular design, continuous evaluation of practice, use of e-learning, and the students\u27 learning processes. The program pilot starts July 2019. This paper will describe the new model, the design-based research method being used, report on the steps completed to date, introduce new sets of data on the new model, analyse the data, evaluate its impact, and result in the next iteration of design improvement. It will primarily focus on program development and the research approach for evaluation of the education model

Understanding Learners\u27 Motivation through Machine Learning Analysis on Reflection Writing

Educational data mining (EDM) is an emerging interdisciplinary field that utilizes a machine learning (ML) algorithm to collect and analyze educational data, aiming to better predict students\u27 performance and retention. In this WIP paper, we report our methodology and preliminary results from utilizing a ML program to assess students’ motivation through their upper-division years in the XYZ project-based learning (PBL) program. ML, or more specifically, the clustering algorithm, opens the door to processing large amounts of student-written artifacts, such as reflection journals, project reports, and written assignments, and then identifies keywords that signal their levels of motivation (i.e., extrinsic vs. intrinsic). These results will be compared against other measures of motivation, including student self-report, faculty observation, and externally validated surveys. As part of a longer-term study, this pilot work sheds light on the key question for student success and retention: how does student motivation evolve through the 3rd and 4th years in college? The purpose of this research project is to gain insights into learners’ motivation levels and how it evolves during the last two years in college, as well as to extend current Educational Data Mining research and Machine Learning analysis described in the literature. It is significant on two fronts: 1) we will extend the ability of ML in analyzing reflective written artifacts to explore student physiological and emotional development; 2) the longitudinal study will help monitor the progressive change of motivation in college students in a PBL environment. Preliminary results from an initial pilot study are promising. By analyzing written reflection journal entries from previous students, the ML algorithm has differentiated keywords into three student motivation levels: “high”, “neutral” and “low”. Using supervised classes, for example, the ML algorithm differentiated words in the highly motivated student text such as “team” and “learning”, while the text coded as low motivation included “use”, “pushed” and “nothing”. For our future research, we aim to create a dictionary that identifies words/phrases related to positive/negative motivation. We will extend the pilot study to a longitudinal evaluation of student motivation over four semesters of engineering education as well as prediction of student success in a PBL environment

Transmembrane TM3b of Mechanosensitive Channel MscS Interacts With Cytoplasmic Domain Cyto-Helix

The mechanosensitive channel MscS functions as an osmolyte emergency release-valve in the event of a sudden decrease in external environmental osmolarity. MscS has served as a paradigm for studying how channel proteins detect and respond to mechanical stimuli. However, the inter-domain interactions and structural rearrangements that occur in the MscS gating process remain largely unknown. Here, we determined the interactions between the transmembrane domain and cytoplasmic domain of MscS. Using in vivo cellular viability, single-channel electrophysiological recording, and cysteine disulfide trapping, we demonstrated that N117 of the TM3b helix and N167 of the Cyto-helix are critical residues that function at the TM3b-Cyto helix interface. In vivo downshock assays showed that double cysteine substitution at N117 and N167 failed to rescue the osmotic-lysis phenotype of cells in acute osmotic downshock. Single-channel recordings demonstrated that cysteine cross-linking of N117C and N167C led to a non-conductive channel. Consistently, coordination of the histidines of N117H and N167H caused a decrease in channel gating. Moreover, cross-linked N117 and N167 altered the gating of the severe gain-of-function mutant L109S. Our results demonstrate that N117–N167 interactions stabilize the inactivation state by an association of TM3b segments with β-domains of the cytoplasmic region, providing further insights into the gating mechanism of the MscS channel

Programmable and Multifunctional DNA-Based Materials for Biomedical Applications

DNA encodes the genetic information; recently, it has also become a key player in material science. Given the specific Watson–Crick base‐pairing interactions between only four types of nucleotides, well‐designed DNA self‐assembly can be programmable and predictable. Stem‐loops, sticky ends, Holliday junctions, DNA tiles, and lattices are typical motifs for forming DNA‐based structures. The oligonucleotides experience thermal annealing in a near‐neutral buffer containing a divalent cation (usually Mg2+) to produce a variety of DNA nanostructures. These structures not only show beautiful landscape, but can also be endowed with multifaceted functionalities. This Review begins with the fundamental characterization and evolutionary trajectory of DNA‐based artificial structures, but concentrates on their biomedical applications. The coverage spans from controlled drug delivery to high therapeutic profile and accurate diagnosis. A variety of DNA‐based materials, including aptamers, hydrogels, origamis, and tetrahedrons, are widely utilized in different biomedical fields. In addition, to achieve better performance and functionality, material hybridization is widely witnessed, and DNA nanostructure modification is also discussed. Although there are impressive advances and high expectations, the development of DNA‐based structures/technologies is still hindered by several commonly recognized challenges, such as nuclease instability, lack of pharmacokinetics data, and relatively high synthesis cost. </p

2018 Scholars at Work Conference Program

Program for the 2018 Scholars at Work Conference at Minnesota State University, Mankato on March 30, 2018

Mifepristone Increases the Cytotoxicity of Uterine Natural Killer Cells by Acting as a Glucocorticoid Antagonist via ERK Activation

Background: Mifepristone (RU486), a potent antagonist of progesterone and glucocorticoids, is involved in immune regulation. Our previous studies demonstrated that mifepristone directly augments the cytotoxicity of human uterine natural killer (uNK) cells. However, the mechanism responsible for this increase in cytotoxicity is not known. Here, we explored whether the increased cytotoxicity in uNK cells produced by mifepristone is due to either anti-progesterone or anti-glucocorticoid activity, and also investigated relevant changes in the mitogen-activated protein kinase (MAPK) pathway. Methodology/Principal Findings: Uterine NK cells were isolated from decidual samples and incubated with different concentrations of progesterone, cortisol, or mifepristone. The cytotoxicity and perforin expression of uNK cells were detected by mitochondrial lactate dehydrogenase-based MTS staining and flow cytometry assays, respectively. Phosphorylation of components of the MAPK signaling pathway was detected by Western blot. Cortisol attenuated uNK cell-mediated cytotoxicity in a concentration-dependent manner whereas progesterone had no effect. Mifepristone alone increased the cytotoxicity and perforin expression of uNK cells; these effects were blocked by cortisol. Furthermore, mifepristone increased the phosphorylation of ERK1/2 in a cortisol-reversible manner. Specific ERK1/2 inhibitor PD98059 or U0126 blocked cortisol- and mifepristone-induced responses in uNK cells

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe