Thursday, September 22, 2016

Use of statistics packages Statistics in science as a whole is a mess. Ecology is no different from the rest of the field, although maybe slightly better than some parts. Statistical analysis is becoming more sophisticated but one thing is clear - R is winning the race.

The mismatch between current statistical practice and doctoral training in ecology. Ecosphere 17th August 2016. doi: 10.1002/ecs2.1394
Ecologists are studying increasingly complex and important issues such as climate change and ecosystem services. These topics often involve large data sets and the application of complicated quantitative models. We evaluated changes in statistics used by ecologists by searching nearly 20,000 published articles in ecology from 1990 to 2013. We found that there has been a rise in sophisticated and computationally intensive statistical techniques such as mixed effects models and Bayesian statistics and a decline in reliance on approaches such as ANOVA or t tests. Similarly, ecologists have shifted away from software such as SAS and SPSS to the open source program R. We also searched the published curricula and syllabi of 154 doctoral programs in the United States and found that despite obvious changes in the statistical practices of ecologists, more than one-third of doctoral programs showed no record of required or optional statistics classes. Approximately one-quarter of programs did require a statistics course, but most of those did not cover contemporary statistical philosophy or advanced techniques. Only one-third of doctoral programs surveyed even listed an optional course that teaches some aspect of contemporary statistics. We call for graduate programs to lead the charge in improving training of future ecologists with skills needed to address and understand the ecological challenges facing humanity.

Friday, September 16, 2016

Motivation to learn

Motivation Five theories:
  1. Expectancy-value
  2. Attribution
  3. Social-cognitive
  4. Goal orientation
  5. Self-determination

Motivation to learn: an overview of contemporary theories. Medical Education 15 September 2016 doi: 10.1111/medu.13074
Objective: To succinctly summarise five contemporary theories about motivation to learn, articulate key intersections and distinctions among these theories, and identify important considerations for future research.
Results: Motivation has been defined as the process whereby goal-directed activities are initiated and sustained. In expectancy-value theory, motivation is a function of the expectation of success and perceived value. Attribution theory focuses on the causal attributions learners create to explain the results of an activity, and classifies these in terms of their locus, stability and controllability. Social- cognitive theory emphasises self-efficacy as the primary driver of motivated action, and also identifies cues that influence future self-efficacy and support self-regulated learning. Goal orientation theory suggests that learners tend to engage in tasks with concerns about mastering the content (mastery goal, arising from a ‘growth’ mindset regarding intelligence and learning) or about doing better than others or avoiding failure (performance goals, arising from a ‘fixed’ mindset). Finally, self-determination theory proposes that optimal performance results from actions motivated by intrinsic interests or by extrinsic values that have become integrated and internalised. Satisfying basic psychosocial needs of autonomy, competence and relatedness promotes such motivation. Looking across all five theories, we note recurrent themes of competence, value, attributions, and interactions between individuals and the learning context.
Conclusions: To avoid conceptual confusion, and perhaps more importantly to maximise the theory-building potential of their work, researchers must be careful (and precise) in how they define, operationalise and measure different motivational constructs. We suggest that motivation research continue to build theory and extend it to health professions domains, identify key outcomes and outcome measures, and test practical educational applications of the principles thus derived.

Nicely contextualizes Carol Dweck's work.

Thursday, September 15, 2016

Assessing Student Learning

Assessing Student Learning For those of us involved in curriculum redesign, the University of Leicester Learning Institute has put together a useful page on assessment and feedback in the form of a "traffic lights" system for a wide range of assessment strategies (not just essays and exams!). You may or may not agree with all their assessments of how long each form of assessment takes, but overall it's a very useful "thinking aid" when you are trying to redesign assessments - worth bookmarking.