{"title":"谁能更准确地预测自己的成绩?本科生数学课程自我评估行为调查","authors":"Kedar Nepal, Ram C. Kafle","doi":"10.1007/s11409-024-09381-2","DOIUrl":null,"url":null,"abstract":"<p>We collected data on students’ self-assessment behavior from four sections of a Calculus II course. Students were asked to write their expected scores on each of the weekly in-class quizzes and problems in the exams, immediately after they completed them. They were then asked to justify their expectation in writing. One-on-one interviews were conducted with a purposefully selected sample of students. During the interviews, they were asked to explain their perceived reasons for their self-assessment behaviors. While the results from quantitative analysis seemed to partially reinforce the findings of existing research that low performers generally overestimate, high performers underestimate their performance, and those in-between performers were (almost) accurate predictors, results from qualitative analysis provided additional insights into their self-assessment behaviors. After analyzing qualitative data, we identified five categories of student behavior: knowing about knowing (KK), not knowing about knowing (NKK), knowing about not knowing (KNK), knowing something is not known but not sure what (KBNKW), and not knowing about not knowing (NKNK). The quantitative analysis showed that students exhibited greater accuracy in assessing their performance when they belonged to the categories KK, KNK, and KBNKW, while their accuracy was lower when they fell into the categories NKNK and NKK. In other words, students who had greater awareness of their level of knowledge were more accurate in predicting their scores compared to their peers, irrespective of their actual performance levels. The logistic regression model revealed a substantial increase in the likelihood of underperforming students overestimating their performance compared to their high-performing counterparts.</p>","PeriodicalId":47385,"journal":{"name":"Metacognition and Learning","volume":"17 1","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Who can predict their performance more accurately? An investigation of undergraduate students’ self-assessment behavior in mathematics courses\",\"authors\":\"Kedar Nepal, Ram C. Kafle\",\"doi\":\"10.1007/s11409-024-09381-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>We collected data on students’ self-assessment behavior from four sections of a Calculus II course. Students were asked to write their expected scores on each of the weekly in-class quizzes and problems in the exams, immediately after they completed them. They were then asked to justify their expectation in writing. One-on-one interviews were conducted with a purposefully selected sample of students. During the interviews, they were asked to explain their perceived reasons for their self-assessment behaviors. While the results from quantitative analysis seemed to partially reinforce the findings of existing research that low performers generally overestimate, high performers underestimate their performance, and those in-between performers were (almost) accurate predictors, results from qualitative analysis provided additional insights into their self-assessment behaviors. After analyzing qualitative data, we identified five categories of student behavior: knowing about knowing (KK), not knowing about knowing (NKK), knowing about not knowing (KNK), knowing something is not known but not sure what (KBNKW), and not knowing about not knowing (NKNK). The quantitative analysis showed that students exhibited greater accuracy in assessing their performance when they belonged to the categories KK, KNK, and KBNKW, while their accuracy was lower when they fell into the categories NKNK and NKK. In other words, students who had greater awareness of their level of knowledge were more accurate in predicting their scores compared to their peers, irrespective of their actual performance levels. The logistic regression model revealed a substantial increase in the likelihood of underperforming students overestimating their performance compared to their high-performing counterparts.</p>\",\"PeriodicalId\":47385,\"journal\":{\"name\":\"Metacognition and Learning\",\"volume\":\"17 1\",\"pages\":\"\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-03-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Metacognition and Learning\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://doi.org/10.1007/s11409-024-09381-2\",\"RegionNum\":2,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Metacognition and Learning","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1007/s11409-024-09381-2","RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
Who can predict their performance more accurately? An investigation of undergraduate students’ self-assessment behavior in mathematics courses
We collected data on students’ self-assessment behavior from four sections of a Calculus II course. Students were asked to write their expected scores on each of the weekly in-class quizzes and problems in the exams, immediately after they completed them. They were then asked to justify their expectation in writing. One-on-one interviews were conducted with a purposefully selected sample of students. During the interviews, they were asked to explain their perceived reasons for their self-assessment behaviors. While the results from quantitative analysis seemed to partially reinforce the findings of existing research that low performers generally overestimate, high performers underestimate their performance, and those in-between performers were (almost) accurate predictors, results from qualitative analysis provided additional insights into their self-assessment behaviors. After analyzing qualitative data, we identified five categories of student behavior: knowing about knowing (KK), not knowing about knowing (NKK), knowing about not knowing (KNK), knowing something is not known but not sure what (KBNKW), and not knowing about not knowing (NKNK). The quantitative analysis showed that students exhibited greater accuracy in assessing their performance when they belonged to the categories KK, KNK, and KBNKW, while their accuracy was lower when they fell into the categories NKNK and NKK. In other words, students who had greater awareness of their level of knowledge were more accurate in predicting their scores compared to their peers, irrespective of their actual performance levels. The logistic regression model revealed a substantial increase in the likelihood of underperforming students overestimating their performance compared to their high-performing counterparts.
期刊介绍:
The journal "Metacognition and Learning" addresses various components of metacognition, such as metacognitive awareness, experiences, knowledge, and executive skills.
Both general metacognition as well as domain-specific metacognitions in various task domains (mathematics, physics, reading, writing etc.) are considered. Papers may address fundamental theoretical issues, measurement issues regarding both quantitative and qualitative methods, as well as empirical studies about individual differences in metacognition, relations with other learner characteristics and learning strategies, developmental issues, the training of metacognition components in learning, and the teacher’s role in metacognition training. Studies highlighting the role of metacognition in self- or co-regulated learning as well as its relations with motivation and affect are also welcomed.
Submitted papers are judged on theoretical relevance, methodological thoroughness, and appeal to an international audience. The journal aims for a high academic standard with relevance to the field of educational practices.
One restriction is that papers should pertain to the role of metacognition in learning situations. Self-regulation in clinical settings, such as coping with phobia or anxiety outside learning situations, is beyond the scope of the journal.