Understanding the Multidimensional Nature of Student Engagement During the First Year of Higher Education

Diversity of Strategies for Motivation in Learning (DSML)—A New Measure for Measuring Student Academic Motivation

Although the Motivated Strategies for Learning Questionnaire (MSLQ) has been widely used to measure student motivation, researchers have raised questions regarding its length and several problematic statements. This study introduces a new questionnaire, adapting items from the MSLQ and including three new key themes of course utility, procrastination and use of diverse sources. A total of 1246 students from a university in the northwest of England, studying a range of subjects and from across all grade boundaries, fully completed the questionnaire. Factor analysis suggested a 24-item questionnaire, including 6 factors: test anxiety, self-efficacy, source diversity, study skills, self-regulation and course utility. The measure, Diversity of Strategies for Motivation in Learning (DSML), has good predictive power for students with or without academic successes, and it can be used as a quick and an early alert monitoring tool to measure student motivation and study skills. The DSML has supported various interventions; however, further testing is required in other cultures, languages and educational environments (such as schools and colleges).

Improving Instructional Fitness Requires Change

Transmission of information has benefitted from a breathtaking level of innovation and change over the past 20 years; however, instructional methods within colleges and universities have been slow to change. In the article, we present a novel framework to structure conversations that encourage innovation, change, and improvement in our system of higher education, in general, and our system of biology education, specifically. In particular, we propose that a conceptual model based on evolutionary landscapes in which fitness is replaced by educational effectiveness would encourage educational improvement by helping to visualize the multidimensional nature of education and learning, acknowledge the complexity and dynamism of the educational landscape, encourage collaboration, and stimulate experimental thinking about how new approaches and methodology could take various fields associated with learning, to more universal fitness optima. The framework also would encourage development and implementation of new techniques and persistence through less efficient or effective valleys of death.

All Happy Emotions Are Alike but Every Unhappy Emotion Is Unhappy in Its Own Way: A Network Perspective to Academic Emotions

Quantitative research into the nature of academic emotions has thus far been dominated by factor analyses of questionnaire data. Recently, psychometric network analysis has arisen as an alternative method of conceptualizing the composition of psychological phenomena such as emotions: while factor models view emotions as underlying causes of affects, cognitions and behavior, in network models psychological phenomena are viewed as arising from the interactions of their component parts. We argue that the network perspective is of interest to studies of academic emotions due to its compatibility with the theoretical assumptions of the control value theory of academic emotions. In this contribution we assess the structure of a Finnish questionnaire of academic emotions using both network analysis and exploratory factor analysis on cross-sectional data obtained during a single course. The global correlational structure of the network, investigated using the spinglass community detection analysis, differed from the results of the factor analysis mainly in that positive emotions were grouped in one community but loaded on different factors. Local associations between pairs of variables in the network model may arise due to different reasons, such as variable A causing variation in variable B or vice versa, or due to a latent variable affecting both. We view the relationship between feelings of self-efficacy and the other emotions as causal hypotheses, and argue that strengthening the students' self-efficacy may have a beneficial effect on the rest of the emotions they experienced on the course. Other local associations in the network model are argued to arise due to unmodeled latent variables. Future psychometric studies may benefit from combining network models and factor models in researching the structure of academic emotions.