quantum computing education

 

https://www.qubitbyqubit.org/

GWAS

 

https://twitter.com/Jazlyn_Mooney/status/1403009120152612868?s=20

college science education assessment

 

Characterizing College Science Assessments: The Three-Dimensional Learning Assessment Protocol

• James T. Laverty ,
• Sonia M. Underwood,
• Rebecca L. Matz,
• Lynmarie A. Posey,
• Justin H. Carmel,
• Marcos D. Caballero,
• Cori L. Fata-Hartley,
• Diane Ebert-May,
• Sarah E. Jardeleza,
• Melanie M. Cooper

• Published: September 8, 2016
• https://doi.org/10.1371/journal.pone.0162333

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0162333

Peer-led Team Learning (PLTL)

 Active Learning in STEM Webinar

 

Title: A Little Help from My Friends: Peer Led Team Learning Before and After COVID-19

Speaker: Eric J. Voss, Ph.D., SIU-Edwardsville

 

Abstract: Peer-led Team Learning (PLTL) is a model of active learning that introduces peer-led workshops as an integral part of undergraduate STEM courses.  Students who have done well in the course are recruited and trained to become peer-leaders.  The peer-leaders meet with small groups of six to ten students each week for one hour to discuss, debate, and engage in problem solving related to the course material.  PLTL originated in a General Chemistry course at the City College of New York in the 1990s.  Early evidence of improved student attitudes and performance led to further study and development of PLTL by a national team, which resulted in more widespread adoption of PLTL in a variety of science, mathematics, and engineering courses.  Very early on, several SIUE chemistry faculty members attended PLTL training sessions sponsored by the National Science Foundation and subsequently implemented PLTL workshops into the SIUE on-sequence General Chemistry courses.  Since then, implementation has expanded into all first-year chemistry courses and several biology courses.  Due to COVID 19, PLTL workshops have transitioned from face-to-face to online synchronous sessions, with new challenges and opportunities.  Student performance data, student attitudes, peer-leader training methods, workshop material development, scheduling, space allocation, institutionalization, and sustainability of PLTL will be discussed in this webinar.

 

bit.ly/TFPlayground

 tensorflow Playground

bit.ly/TFPlayground

https://playground.tensorflow.org/

data science website, Randy Lao

free books, and good cartoon
www.claoudml.com

Rosalind education platform, bioinformatics, problem sets

http://rosalind.info/problems/list-view/

CourseSource

Course Source, undergraduate

http://www.coursesource.org/courses/bioinformatics

important concepts/skills in undergraduate QBIO education and training

Important concepts

• Modeling approach:
•  ODE
•  PDE
•  Discrete
•  Analytic versus simulation
• Bistability, bifurcation
• Visualization of quantitative models
• 
  

App to estimate volume, conver units.

Many app exists for unit conversions

photo, video volume estimation

handwriting recognition apps

making student thinking and learning visible

Ken Shelton

Socrative

Student Portfolios

Assessment for, as, of Learning
For learning (Formative)
Of Learning (summative)
As Learning (Reflective)
http://www.tvdsb.ca/webpages/takahashid/techdia.cfm?subpage=128207

www.govote.at
https://www.menti.com/2ebdbb/3#
https://www.mentimeter.com/?utm_campaign=mentimeter%20logo&utm_medium=web-link&utm_source=govote

Digital Portfolio can let students

Desmos Calculator

math education

https://www.desmos.com/calculator

ZSpacve, 3D virtual reality learning

Virtual reality

http://zspace.com/

todo: lectin hemoglobin modeling

lectinhemoglobin, optimal oxygen concentration, respiratory, o

rasperry pi,

4273 pi

https://4273pi.org/loan-boxes/

NSF computational education programs

CS for all, computer science for all

STEM+C (computing partnership)

ITEST (innovative technology experience for students and teachers)
https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=5467


https://www.nsf.gov/awardsearch/showAward?AWD_ID=1311902&HistoricalAwards=false

Cognitive Load Theory and Computer Science Education John Sweller,

Cognitive Load Theory and Computer Science Education 

John Sweller, Emeritus Faculty, University of New South Wales 

Cognitive load theory uses our knowledge of human cognitive architecture to devise instructional procedures, most of which are directly relevant to computer science education. There are several basic aspects of human cognition that are critical to instructional design. First, based on evolutionary educational psychology, cognitive load theory assumes that most topics taught in educational and training institutions are ones that we have not specifically evolved to learn. Such topics require biologically secondary knowledge rather than the biologically primary knowledge that we have evolved to acquire. Second, these instructionally relevant topics require learners to acquire domain-specific rather than generic cognitive skills. Third, while generic cognitive knowledge does not require explicit instruction because we have evolved to acquire it, domain-specific concepts and skills that provide the content of educational syllabi, do require explicit instruction. These three factors interact with the well-known capacity and duration constraints of working memory and the unlimited capacity and duration characteristics of long-term memory to delineate a cognitive architecture relevant to instructional design. The working memory limits do not apply to biologically primary, generic knowledge acquired without explicit instruction but do apply to the biologically secondary, domain-specific knowledge that requires explicit instruction and that is relevant to computer science education. Human cognition when dealing with such knowledge constitutes a natural information processing system that has evolved to mimic the architecture of biological evolution. Cognitive load theory uses this architecture to generate a large range of instructional effects concerned with procedures for reducing extraneous working memory load in order to facilitate the acquisition of knowledge in long-term memory. This talk reviews the theory and indicates the instructional implications relevant to computer education. 

https://www.youtube.com/watch?v=0xm9_g699fg

last 50 years of scientific research,

http://dcscience.net/Lawrence-2016.pdf

The Last 50 Years: Mismeasurement and Mismanagement Are Impeding Scientific Research

 

 

Peter A. Lawrence

Department of Zoology, University of Cambridge, Cambridge, United Kingdom

 

*** phylogenetic comparative method

Modern Phylogenetic Comparative Method

http://www.mpcm-evolution.org/

note taking for computational experiments

Many students did not to take notes during computational experiments.