As an educational psychologist and educator for the past 20 years, I’ve come to appreciate the claims of constructivism more and more. In particular, I’m a firm believer that students learn by doing at a much higher level of quality than when they just sit in the classroom and listen to somebody else tell them what the facts are.
Counter to what you may think, the approaching end of school does not mean educators are ready to celebrate. OK, maybe a little. This stereotypical response to the closing of school years misses something very important. Endings can be very difficult.
Our devices track our behavior in ways that create unimaginable amounts of information, information that can be useful for understanding human behavior. Not surprisingly, the widespread availability of these data parallels an increasing interest in data science and related topics (see Figure 1). Social scientists are increasingly using “big data” to examine theoretically-grounded research questions. Yet, few social scientists, especially psychologists, have the skills and training experiences needed to engage with this rapidly growing area of expertise called “data science.”
Much like a crock pot used to cook large meals with minimal effort, data science can be used to improve efficiency, quality, and edibility of your products. Automation, simple algorithms, and machine learning (i.e. using data to create predictive models) are all tools that can be utilized to increase efficiency and accuracy and reduce costs, while visualization techniques help to communicate your analyses clearly and in an easy to digest way.
What makes someone with early potential develop that talent in a way that results in high performance or greatness?
A new volume, The Psychology of High Performance: Developing Human Potential into Domain-Specific Talent, edited by Rena F. Subotnik, Paula Olszewski-Kubilius, and Frank C. Worrell, addresses that question by examining outstanding performance across five different domains: academic disciplines (mathematics and psychology), arts production (culinary arts and drawing/painting), arts performance (dance and acting), professions (medicine, software engineering, and professional teams), and sport (golf and team sports).
Contemporary challenges — such as climate change, and food, energy and water security — demand that people think scientifically. These challenges are often complex and interrelated; for example, society’s increasing demand for energy contributes to human-induced climate change, which in turn, limits freshwater and food supplies (Wheeler & Von Braun, 2013). While many socio-scientific challenges are seriously impacting local, regional and global communities, an increasing availability of information has contributed to what many call a “Post-Truth Era,” where emotions and personal beliefs override scientifically valid evidence and explanations. Combating mythological and unproductive thinking in the face of current change requires increased scientific literacy, which involves knowing both what scientists know and how scientists come to know what they know.
Recently, in my educational psychology class, I overheard two students talking about how they were right-brain thinkers (i.e., are holistic thinkers, problem solve using intuition, creative, etc.).
Two weeks later, I heard my one of my developmental research methods students say, “I think the best way to study is to use color-coded highlighters” followed by “It also helps me on tests when I listen to lectures because I am an auditory learner.”
For many students graduation is a few months away. Unfortunately, chances are many of them haven’t taken the necessary steps to prepare for life after college. Of course, some of them will be on their way to graduate school. But what about the roughly 72 percent of students (National Association of Colleges and Employers, 2018) who will be directly entering the workforce?
As a new teacher, I spent most of my time focused on instruction, my primary concern being what I needed to teach and how I was going to teach it. Assessment and grading was at best a nuisance and at worst a necessary requirement. Needless to say, my consideration of assessment and grading was an afterthought, usually poorly developed and poorly constructed measurements of student learning.
