Well-designed instructional videos are powerful tools for helping students learn and prompting students to use
generative strategies while learning from videos further bolsters their effectiveness. However, little is known
about how individual differences in motivational factors, such as achievement goals, relate to how students learn
within multimedia environments that include instructional videos and generative strategies. Therefore, in this
study, we explored how achievement goals predicted undergraduate students’ behaviors when learning with
instructional videos that required students to answer practice questions between videos, as well as how those
activities predicted subsequent unit exam performance one week later. Additionally, we tested the best measurement models for modeling achievement goals between traditional confirmatory factor analysis and bifactor confirmatory factor analysis. The bifactor model fit our data best and was used for all subsequent analyses. Results indicated that stronger mastery goal endorsement predicted performance on the practice questions in the multimedia learning environment, which in turn positively predicted unit exam performance. In addition, students’ time spent watching videos positively predicted practice question performance. Taken together, this research emphasizes the availing role of adaptive motivations, like mastery goals, in learning from instructional videos that prompt the use of generative learning strategies.
more »
« less
Investigating bifactor modeling of biology undergraduates’ task values and achievement goals across semesters.
Undergraduate science, technology, engineering, and mathematics (STEM) students’ motivations have a strong influence on whether and how they will persist through challenging coursework and into STEM careers. Proper conceptualization and measurement of motivation constructs, such as students’ expectancies and per- ceptions of value and cost (i.e., expectancy value theory [EVT]) and their goals (i.e., achievement goal theory [AGT]), are necessary to understand and enhance STEM persistence and success. Research findings suggest the importance of exploring multiple measurement models for motivation constructs, including traditional con- firmatory factor analysis, exploratory structural equation models (ESEM), and bifactor models, but more research is needed to determine whether the same model fits best across time and context. As such, we mea- sured undergraduate biology students’ EVT and AGT motivations and investigated which measurement model best fit the data, and whether measurement invariance held, across three semesters. Having determined the best- fitting measurement model and type of invariance, we used scores from the best performing model to predict biology achievement. Measurement results indicated a bifactor-ESEM model had the best data-model fit for EVT and an ESEM model had the best data-model fit for AGT, with evidence of measurement invariance across semesters. Motivation factors, in particular attainment value and subjective task value, predicted small yet statistically significant amounts of variance in biology course outcomes each semester. Our findings provide support for using modern measurement models to capture students’ STEM motivations and potentially refine conceptualizations of them. Such future research will enhance educators’ ability to benevolently monitor and support students’ motivation, and enhance STEM performance and career success.
more »
« less
- Award ID(s):
- 1821594
- NSF-PAR ID:
- 10487839
- Publisher / Repository:
- American Psychological Association
- Date Published:
- Journal Name:
- Journal of Educational Psychology
- Volume:
- 115
- Issue:
- 6
- ISSN:
- 0022-0663
- Page Range / eLocation ID:
- 836 to 858
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract This study examined factors that have been shown to predict middle school students' (grades 6–8) task values (utility value related to engaging with science in the future). Using structural equation modeling students' (
N = 1015) responses to a career aspiration survey were analyzed to determine relationships among the factors. The model of best fit showed that factors related to science capital (Exposure to STEM Practitioners, STEM Experiences, and Tool Access) are key in shaping both students' Science Achievement Value and their Perceptions of Family Task Achievement Value. Further, both Science Achievement Value and Perceptions of Family Science Achievement Value have a direct positive effect on youth Future Science Task Value. These findings document important ways in which youth access and exposure to science experiences, tools, and to scientists lay the groundwork for science motivation and ultimately to valuing future science tasks. -
null (Ed.)The current COVID-19 pandemic has forced many colleges and universities to remain on a completely online or remote educational learning environment for the 2020 Spring and Fall semesters, however there is a growing concern in STEM fields about how students will be able to achieve one of the major ABET learning outcomes without conducting physical, hands on laboratory exercises as many STEM disciplines are switching to virtual laboratory; an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering/scientific judgment to draw conclusions. In addition to the limited achievement of the ABET outcomes, roughly half of the population of a historically black university communicated their anxieties during the pandemic to the University President via Change.org. The students’ main anxiety is portrayed in a statement culled from the petition as follows: “Most classes are very hands-on, and we are not able to do those from home because of the limited resources available at home”. This paper highlights the best practices for the implementation of home-based hands-on activities across multiple STEM fields. The paper further elaborates on the impact of remote and virtual labs on students’ attitude, interest, and performance in STEM over the home-based hands-on experimentation. Home-based hands-on laboratory activities were performed in biology, electrical engineering, industrial engineering, transportation system, and civil engineering. The results of a Motivated Strategies for Learning Questionnaires (MLSQ) survey that was administered to about 100 STEM students revealed better gains in key constructs associated with student success, such as motivation, critical thinking, and metacognition.more » « less
-
This research is focused on the adoption of an experiment-centric teaching approach to enhance student’s learning, develop their critical thinking skills, and help students better understand the underlying concepts in biology, thereby giving them a better comprehension of how these concepts may be applied in practice as well as facilitating their academic success. Traditional and experiment-centric teaching methods are used to instruct students in four biology courses (BIO 101-W09 Introductory Biology for Non-Major, Bio 103 Introductory Biology for Nursing Major, BIO 109-001 Foundations in Biology, Diversity, and Organismal Systems and Bio 201. Anatomy and Physiology I) taken by first and second-year students. The sensor from a heart rate app is used to conduct and monitor several common experiments in Biology. On phone screens, the data gathered from these experiments can be visualized in real-time. In order to measure the key constructs associated with students’ success (motivation, epistemic and perceptual curiosity, and self-efficacy), data collection was done pre-and post-implementation of the experiments using the Motivated Strategies for Learning Questionnaire (MSLQ) developed by Pintrich, Smith, García, and McKeachie, in 1991. Also, the Classroom Observation Protocol for Undergraduate STEM (COPUS) was employed to characterize the simultaneous activities of instructors and learners during class sessions. More so, students’ understanding of the course and how they process instructions were evaluated using signature assignments. Data analysis would be conducted using Statistical Package for Social Scientists (SPSS 25.0). This research would carefully investigate the association between students’ motivation and their gender at 95% confidence level.more » « less
-
more » « less
Abstract Background To accurately measure students' science, technology, engineering and mathematics (STEM) career interest, researchers must get inside the ‘black box’ to understand students' conceptualizations of STEM careers.
Aims The aim of Study 1 was to explore whether students' conceptualizations of STEM included medical careers. The aim of Study 2 was to explore whether predictors of STEM career interest (e.g., gender and motivation) varied by STEM definition (inclusion/exclusion of medical careers).
Samples In Study 1, the sample was US college students (
N = 125) who were mostly White (80%). In Study 2, the sample was US 10th‐grade high school students (N = 455) who were mostly Black (79%).Methods In Study 1, students completed an online questionnaire. In Study 2, students completed various measures of math achievement, motivation (science and math expectancies of success, interest and importance value) and career interest with an importance.
Results In Study 1, medical careers were less often classified as STEM careers than traditional STEM careers, but more often classified as STEM than non‐STEM careers. In Study 2, science importance value was the only motivational predictor of students' STEM+Medicine career interest, and no motivation constructs predicted traditional STEM career interest. Boys expressed greater interest in traditional STEM careers, while girls expressed greater interest in STEM+Medicine careers.
Conclusions Students' conceptualizations of STEM are not binary. Thus, we recommend researchers are explicit about their definition of STEM with study participants, in their coding and in their publications.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1037/edu0000803
