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Effects of affiliation-, achievement-, and power-related topics in mathematical word problems on students' performance, task-related values, and expectancies.

ABSTRACT: A motivational downturn in mathematics during secondary school has been well documented for many students. As a way to address this, creating personally relevant tasks is supposed to increase students' motivation for mathematical tasks. According to recent research, topics relating to affiliation, achievement, and power are personally relevant for young people. Prior research showed that motive imagery in school tasks increases students' task-related intrinsic value and success expectancies. The present study examined the effect of motive topics in word problems on students' task performance. We contextualized mathematical tasks either with affiliation, achievement, and power topics or with neutral topics not related to motive topics. The tasks were comparable regarding their mean word count, text, and mathematical complexity. In three experimental studies (N1 = 56, N2 = 63, N3 = 62), the students solved four tasks for each motive topic and neutral tasks, respectively. The dependent variables were task performance, intrinsic values, and expectancies of success. Repeated measures analyses of variance with the four-level, within-subjects factor motive imagery revealed positive effects of motive imagery in word problems on students' task performance. This was particularly true for achievement-related tasks compared with neutral tasks. The results also indicated slightly positive effects for affiliation-related word problems on students' performance. In addition, the intrinsic value for affiliation-related (Experiment 1) and achievement-related tasks (Experiment 3) was higher than for neutral tasks. Power imagery did not affect students' task performance; it negatively affected students' intrinsic value compared with neutral tasks. Task-related success expectancies were not influenced by motive imagery. The present study replicates and extends previous findings that indicate that tasks referring to affiliation and achievement imagery are more appealing to students and can benefit their performance.

SUBMITTER: Scheidemann B

PROVIDER: S-EPMC9246239 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Theory Digital technologies have become an integral part of everyday life that children are exposed to. Therefore, it is important for children to acquire an understanding of these technologies early on by teaching them computational thinking (CT) as a part of STEM. However, primary school teachers are often reluctant to teach CT. Expectancy-value theory suggests that motivational components play an important role in teaching and learning. Thus, one hindrance to teachers’ willingness to teach CT might be their low expectancies of success and high emotional costs, e.g., anxiety towards CT. Thus, introducing preservice teachers to CT during their university years might be a promising way to support their expectancies and values, while simultaneously alleviating their emotional costs. Prior CT competences might contribute to these outcomes. Aims We investigated whether a specifically designed seminar on CT affected preservice teachers’ expectancies and values towards programming.Method: A total of 311 German primary school and special education preservice teachers took part in the study. The primary school preservice teachers received a seminar on CT and programming with low-threshold programming tasks, while the special education teachers served as a baseline group. The seminar was specifically designed to enhance expectancies and values and decrease emotional costs, following implications of research on expectancy-value theory. Results The preservice teachers who visited the seminar gained higher expectancies and values towards CT and programming compared to the baseline group. Moreover, their emotional costs decreased. CT was positively related to change in expectancies and values and negatively related to emotional costs. Discussion Interventions with low-threshold programming tasks can support primary school preservice teachers in finding trust in their abilities and values towards CT. Moreover, their anxiety towards CT and programming can be alleviated. Thus, first steps in preparing preservice teachers to teach CT in their future classrooms can be taken in university.

Project description:BackgroundBy the end of 2022, more than 100 million people were infected with COVID-19 in the United States, and the cumulative death rate in rural areas (383.5/100,000) was much higher than in urban areas (280.1/100,000). As the pandemic spread, people used social media platforms to express their opinions and concerns about COVID-19-related topics.ObjectiveThis study aimed to (1) identify the primary COVID-19-related topics in the contiguous United States communicated over Twitter and (2) compare the sentiments urban and rural users expressed about these topics.MethodsWe collected tweets containing geolocation data from May 2020 to January 2022 in the contiguous United States. We relied on the tweets' geolocations to determine if their authors were in an urban or rural setting. We trained multiple word2vec models with several corpora of tweets based on geospatial and timing information. Using a word2vec model built on all tweets, we identified hashtags relevant to COVID-19 and performed hashtag clustering to obtain related topics. We then ran an inference analysis for urban and rural sentiments with respect to the topics based on the similarity between topic hashtags and opinion adjectives in the corresponding urban and rural word2vec models. Finally, we analyzed the temporal trend in sentiments using monthly word2vec models.ResultsWe created a corpus of 407 million tweets, 350 million (86%) of which were posted by users in urban areas, while 18 million (4.4%) were posted by users in rural areas. There were 2666 hashtags related to COVID-19, which clustered into 20 topics. Rural users expressed stronger negative sentiments than urban users about COVID-19 prevention strategies and vaccination (P<.001). Moreover, there was a clear political divide in the perception of politicians by urban and rural users; these users communicated stronger negative sentiments about Republican and Democratic politicians, respectively (P<.001). Regarding misinformation and conspiracy theories, urban users exhibited stronger negative sentiments about the "covidiots" and "China virus" topics, while rural users exhibited stronger negative sentiments about the "Dr. Fauci" and "plandemic" topics. Finally, we observed that urban users' sentiments about the economy appeared to transition from negative to positive in late 2021, which was in line with the US economic recovery.ConclusionsThis study demonstrates there is a statistically significant difference in the sentiments of urban and rural Twitter users regarding a wide range of COVID-19-related topics. This suggests that social media can be relied upon to monitor public sentiment during pandemics in disparate types of regions. This may assist in the geographically targeted deployment of epidemic prevention and management efforts.

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Effects of affiliation-, achievement-, and power-related topics in mathematical word problems on students' performance, task-related values, and expectancies.

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