Supporting data for "Getting Students through the Gates: How Self-efficacy and Interest Trajectories Drive Learning Outcomes in Large Mathematics Courses"

Gateway courses are foundational prerequisite courses that undergraduate students must complete prior to enrolling in major courses (e.g., first-year mathematics, chemistry, psychology, statistics). Gateway courses often have high enrolment, and provide less support, structure, and feedback compared to previous experiences (e.g., secondary school). Declines in students' motivation and performance are common. This PhD project investigated two sources of engagement and motivation: self-efficacy and interest across two mathematics gateway courses. In particular, factors related to how self-efficacy and interest changed during the courses were examined across the studies. Four studies were conducted across five offerings of these two courses from 2020-2022. Participants were students enrolled in these courses. Study 1 (n=175; Sept-Dec 2020; Course 1) was conducted in an online (pandemic) setting. The interplay between students' (amounts of) self-efficacy, interest, and performances (i.e., quizzes) across the course was investigated. Study 2 (n=349; Sept-Dec 2021; Course 1) was conducted the next year, and examined how overall self-efficacy changes, and how those changes were associated with performances across a course, and interest at the end of the course. Study 3 (n=313; Sept-Dec 2021; Course 2) investigated short-term changes in interest, and how they were related to performance, and self-efficacy. Lastly, Study 4 contained two studies (n=299; n=407; Studies 4a, 4b; Courses 1 &amp; 2) that investigated the interplay between perceived difficulty on performance tasks (i.e., quizzes), short-term changes in self-efficacy, performances, and interest (in the second study).The data files are the datasets used to conduct the analyses across the four studies. These included students' responses on formative quizzes, and self-reported data on self-efficacy, interest, perceived difficulty, and gender. These data were used for quantitative analysis using MPlus and other software. Each folder contains the relevant files each study (presented in the respective chapter of the thesis).1) Chapter 3 - Study 1 contains the dataset used for the first study. This study is already published.<br>2) Chapter 4 - Study 2 contains the datasets used for the second study, including for the full model, invariance and reliability testing, and dataset for IRT.<br>3) Chapter 5 - Study 3 contains the datasets used for the third study, including for the full model and dataset for IRT.<br>4) Chapter 6 - Study 4 (Studies 4a and 4b) contains the datasets used for the last study, including those used for the full model, dataset for IRT, and perceived difficulty.

入门必修先修课程（Gateway Courses）是本科学生在修读专业核心课程前必须完成的基础性先修课程（例如大一数学、化学、心理学、统计学课程）。这类课程通常选课人数众多，且相较于此前的学习经历（如中学阶段），所能提供的支持、教学架构与反馈均较为有限。学生学习动机与学业表现下滑的情况较为普遍。
本博士研究针对两门数学入门必修先修课程，探究了影响学习投入与动机的两大核心因素：自我效能感（Self-efficacy）与学习兴趣。各子研究均聚焦于课程进行期间自我效能感与学习兴趣的动态变化相关影响因素。本研究于2020至2022年间，依托这两门课程的五轮授课开展了四项子研究，研究对象为修读该类课程的本科生。
子研究1（样本量n=175；2020年9月-12月；课程1）于疫情期间的线上教学场景中开展，旨在探究课程全程中学生的自我效能感水平、学习兴趣与学业表现（即随堂测验成绩）之间的交互关系。
子研究2（样本量n=349；2021年9月-12月；课程1）于次年开展，聚焦于整体自我效能感的变化趋势，以及该变化与课程全程学业表现、结课学习兴趣之间的关联。
子研究3（样本量n=313；2021年9月-12月；课程2）探究了学习兴趣的短期动态变化，及其与学业表现、自我效能感之间的关联。
最后一项子研究4包含两个分支研究（样本量分别为n=299、n=407；分支研究4a、4b；覆盖课程1与课程2），其中分支研究4b探究了学业任务（即随堂测验）感知难度、自我效能感短期变化、学业表现与学习兴趣之间的交互关系。
本数据集包含四项子研究所需的全部分析数据，具体包括学生形成性随堂测验作答数据，以及关于自我效能感、学习兴趣、任务感知难度与性别的自陈式调查数据。上述数据将借助MPlus及其他统计软件开展量化分析。
各文件夹包含对应子研究的相关数据文件，对应学位论文的相应章节。<br>1) 第3章——子研究1：包含第一项子研究的数据集，该研究已正式发表。<br>2) 第4章——子研究2：包含第二项子研究的数据集，涵盖全模型分析、不变性与信度检验以及项目反应理论（Item Response Theory，简称IRT）分析所需数据集。<br>3) 第5章——子研究3：包含第三项子研究的数据集，涵盖全模型分析以及项目反应理论分析所需数据集。<br>4) 第6章——子研究4（分支研究4a与4b）：包含最后一项子研究的数据集，涵盖全模型分析、项目反应理论分析以及任务感知难度相关分析所需数据集。