Now that I’ve shared general advice for how to structure an NSF proposal and lessons I’ve learned from being an NSF reviewer, I’ll focus on advice specifically for the CAREER program. CAREER is viewed as a prestigious award, and based on my discussions with colleagues, this keeps many people from applying for it. In addition to all of the platitudes about “you miss 100% of the shots you don’t take” or “shoot for the moon and even if you miss you’ll land among the stars,” there’s another great reason to write a CAREER proposal.

A major feature that makes applying for NSF funding unique is their use of external review panels. NSF reviews are conducted primarily by panels of external reviewers who don’t work at the NSF and have other full-time jobs. While these reviewers are representative of your scientific peers, they are a unique audience.

The average reader for a research paper…

• chooses to read the paper
• is an expert in the (sub)field of the paper
• wants to learn something

The average reviewer on an NSF panel…

• agrees to read it on top of their normal workload
• is from a related field, but probably not an expert on your topic
• wants to finish as quickly as possible while being fair

As an NSF CAREER recipient and frequent panelist for NSF reviews, I put together a presentation for my college about strategies for preparing a competitive NSF CAREER proposal. While I think my colleagues are the best, I also wanted to share these strategies more broadly for anyone who is interested in applying, especially those who might get less support from their institution. My experiences are primarily in the EDU (education) directorate with some experience in the CISE (computing) directorate, so some norms or recommendations might not apply globally. I’ll be making three posts: the structure of an NSF application including CAREER-specific criteria, lessons learned from being an NSF reviewer, and EDU-directorate-specific CAREER advice.

NSF CAREER is for early career folks, so this application might be your first NSF application or at least your first application as Principal Investigator (PI). The NSF application has …a lot of pieces, so I wanted to start by reviewing the application process generally, including CAREER-specific criteria, and strategies to complete each piece. Of course, the most comprehensive information will be found in the program solicitation and the PAPPG, but those documents can be intimidating without some advanced organizers.

Motivation 

To examine the interaction of perceived control and testosterone on persistence in the face of defeat.

Perceived Control and Motivation

The amount of control people think they have over something can greatly affect how they experience failure or defeat. For example, if someone is convinced that they can solve a problem, then they are likely to persist in trying to solve the problem, even if they fail at first (see Self-Efficacy and Academic Motivation). Perceived control is interesting because it does not always reflect actual control. It can be manipulated so that people experience high levels of perceived control in situations that are obviously out of their control. People continue to gamble though the house always wins.

This study examined the role of perceived control on persistence in a competition against an opponent that became increasingly difficult to beat. Consistent with the literature, those with high perceived control persisted in the competition longer than those with low perceived control. However, the study also examined the interaction of exogenous testosterone on this effect. Participants (all men) who received exogenous testosterone, regardless of whether they had low or high perceived control, persisted in the competition as long as those who had high perceived control and received a placebo. The results suggest that testosterone increases persistence in the face of defeat, even with low perceived control.

Motivation 

To reflect on three decades of evolving research about fixed vs. growth mindsets, applications and misapplications in education, and interventions that encourage students to challenge themselves.

Fixed vs. Growth Mindsets

Carol Dweck describes fixed vs. growth mindsets as a theory of people’s beliefs about human attributes and how those beliefs affect motivation and achievement. Education researchers primarily use this theory to explain learners’ responses to setbacks, challenges, and failures while developing new knowledge and skills. In a fixed mindset, people believe that abilities are unchanging, and your initial proficiency in an area corresponds to your inherent ability in that area. Thus, when faced with setbacks, they believe that they are not suited to the task. Conversely in a growth mindset, people believe that abilities are malleable, and that you can improve your proficiency in an area regardless of your starting point. Thus, they view challenges as opportunities to develop and improve skills, including skills for which they have a natural proficiency.

Mindsets apply to people’s beliefs about human attributes outside of educational settings. Mindsets include people’s beliefs about skill in professional settings and their personalities. Whether you believe a leader is born or made depends on your mindset. In correlational work exploring the relationship between mindset and achievement, people with a growth mindset tend to achieve more in school and throughout their lives.

Motivation 

To review two decades of research on refutation text research in science education to determine factors that make them more or less effective.

Refutation Texts

Refutation texts are a direct-instruction approach to addressing misconceptions. They are popular in science education because, as people interact with the physical world, they develop misconceptions about how it works. When they are faced with facts that contradict this prior knowledge, they can take one of three paths according to Posner et al.’s (1982) model of conceptual change:

• The least useful path: ignore the new information because it doesn’t fit in existing knowledge structures, and thus, doesn’t make sense (this is not an entirely voluntary process)
• The most common path: develop a separate knowledge structure disconnected from the existing knowledge structure for the new information (and perhaps not realize that they are in conflict)
• The most useful but least common path: reorganize existing knowledge structures to incorporate new information (i.e., conceptual change)

Achieving conceptual change is hard work, and that’s why misconceptions are so difficult to remedy. The need to reorganize prior knowledge structures is why direct-instruction approaches, which are inherently not responsive to individual students’ prior knowledge, are often not productive. For an example, see my article summary on erroneous examples. However, refutation texts have consistently been more effective at addressing misconceptions in science education compared to expository texts, which give correct explanations only. This paper discusses how.

Motivation 

To review 15 years of research on self-efficacy to contrast it with related constructs and examine its effect on academic motivation.

Overview of Self-Efficacy Theory

Self-efficacy is a person’s judgement of their ability to achieve goals or overcome obstacles. According to Bandura’s (1986) self-efficacy theory, learners develop self-efficacy through several different channels. The strongest predictor of self-efficacy is perceived performance and accomplishments. Success, especially on difficult tasks, improves self-efficacy, but receiving external assistance can negate this effect. A weaker contributor to self-efficacy is observing others succeed, especially if the person is perceived to be similar in ability. Similarly, external persuasion and encouragement, especially by role models, can boost self-efficacy temporarily, but it must be accompanied by later accomplishments on authentic tasks to be sustainable. The last source of information that students use to develop self-efficacy is physiological and emotional experiences. If students feel physically sweaty or emotionally anxious while working on problems, these experiences can translate to low self-efficacy. Alternatively, feeling excited or experiencing flow can translate to high self-efficacy.

Motivation 

To consider tradeoffs between learning and performance and examine instructional strategies that support both.

Productive Failure

Kapur researches an instructional strategy called productive failure. Productive failure encourages learners to create incorrect or incomplete solutions, get stuck during problem solving, or otherwise fail to produce a right answer when they first start learning a new procedure. The underlying theory is that this strategy encourages students to try to apply their prior knowledge to the problem, recognize whether it works, and identify the new knowledge they need to complete the solution. Once learners have gone through this process of failing, they are primed to fill in the gaps in their knowledge through instruction. A critical feature of productive failure is that the failure during the problem-solving phase is followed by productive learning during instruction, called the consolidation phase.

A ton of instructors at all levels of education (including adult education) are suddenly being forced to teach through online media as a result of the pandemic. As someone who teaches online and researches online learning, I want to be helpful without being overly prescriptive and making this transition even harder. As many others have pointed out, instructors aren’t engaging in online learning as much as they are suddenly being forced to teach at a distance.

Since many universities have decided to offer summer courses online (and some are looking at the fall), we could be teaching online for a significant time. If you’d like some tips for effective online learning, I’ve compiled a list specifically for this circumstance. I put them roughly in order of importance, so if you want to tackle one each week, start from the top. Continue reading →

Motivation 

To evaluate whether explicit instruction followed by problem solving or problem solving followed by explicit instruction is more effective for later problem-solving performance, especially for procedures that are inherently complex.

Order of Problem Solving and Explicit Instruction

The debate between explicit/direct instruction and minimal instruction is longstanding in problem-solving education. Those who support direct instruction (i.e., explicitly telling the learner everything you want them to know) cite its efficiency for producing gains in problem-solving skills. Those who support minimal instruction (i.e., providing scaffolding to the learner to encourage them to construct problem-solving knowledge themselves) cite the enduring effects of building upon prior knowledge and development of other skills throughout the process. A subgroup has decided both types of instruction are important and now debates in which order learners should receive both types of instruction.

Continue reading →