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The speed-accuracy trade-off (SAT) suggests that time constraints reduce response accuracy. Its relevance in observational settings--where response time (RT) may not be constrained but respondent speed may still vary--is unclear. Using 29 data sets containing data from cognitive tasks, we use a flexible method for identification of the SAT (which we test in extensive simulation studies) to probe whether the SAT holds. We find inconsistent relationships between time and accuracy; marginal increases in time use for an individual do not necessarily predict increases in accuracy. Additionally, the speed-accuracy relationship may depend on the underlying difficulty of the interaction. We also consider the analysis of items and individuals; of particular interest is the observation that respondents who exhibit more within-person variation in response speed are typically of lower ability. We further find that RT is typically a weak predictor of response accuracy. Our findings document a range of empirical phenomena that should inform future modeling of RTs collected in observational settings. [This work was co-written by the Project iLEAD Consortium.]

The purpose of this study was to explore high school course-taking sequences and their relationship to college enrollment. Specifically, we implemented sequence analysis to discover common course-taking trajectories in math, science, and English language arts using high school transcript data from a recent nationally representative survey. Through sequence clustering, we reduced the complexity of the sequences and examined representative course-taking sequences. Classification tree, random forests, and multinomial logistic regression analyses were used to explore the relationship between the course sequences students complete and their postsecondary outcomes. Results showed that distinct representative course-taking sequences can be identified for all students as well as student subgroups. More advanced and complex course-taking sequences were associated with postsecondary enrollment.

Research shows that the intensity of high school course-taking is related to postsecondary outcomes. However, there are various approaches to measuring the intensity of students' course-taking. This study presents new measures of coursework intensity that rely on differing levels of quantity and quality of coursework. We used these new indices to provide a current description of variations in high school course-taking across grades and student subgroups using a nationally representative dataset, the High School Longitudinal Study of 2009. Results showed that for measures emphasizing the quality of coursework the gaps in coursework among underserved students were larger and there was less upward movement in rigor across grades.

This article describes a 4-year study of experienced high school biology teachers' participation in a five-step professional development experience in which they iteratively studied student ideas with the support of a set of learning progressions, designed formative assessment activities, practiced using those activities with their students, enacted the activities, and then reflected on next steps to guide their instruction. Drawing on classroom artifacts and student responses to a pre-post assessment, we examined the alignment of teacher-created formative assessment tasks with the learning progressions, as well as student learning relative to the progressions. A partial-credit Model revealed that the majority of students' learning reflected learning from lower- to upper-anchors on multiple learning progressions. This finding suggests that, by participating in the professional learning experience, teachers were able to successfully support student learning of the content as represented in the majority of the learning progressions. Results are interpreted in light of learning progressions being used as scaffolds for formative assessment design and practice.

Artificial Neural Networks (ANNs) have been proposed as a promising approach for the classification of students into different levels of a psychological attribute hierarchy. Unfortunately, because such classifications typically rely upon internally produced item response patterns that have not been externally validated, the instability of ANN estimates of attribute probabilities may not be widely appreciated. The present study illustrates the problem with both empirical and simulated data. In particular, it is shown that when an ANN is "trained" multiple times using the same data, attribute probability estimates can vary, sometimes dramatically. Researchers hoping to apply ANNs in the context of diagnostic classification models with student test data need to be very deliberate in checking the sensitivity of their findings.

The teaching practices of recognizing and responding to students' ideas during instruction are often called formative assessment, and can be conceptualized by four abilities: designing formative assessment tasks, asking questions to elicit student thinking, interpreting student ideas, and providing feedback that moves student thinking forward. While these practices have been linked to positive learning outcomes for students, designing and enacting formative assessment tasks in science classrooms presents instructional challenges for teachers. This paper reports on the results of a long-term study of high school biology teachers who participated in a 3 year professional development program, called the Formative Assessment Design Cycle (FADC), which guided them to iteratively design, enact, and reflect upon formative assessments for natural selection in school-based teacher learning communities. Nine teachers participated for three academic years; sources of data included teachers' interpreting of student ideas in line with a learning progression, the formative assessment tasks they designed each year of the study, videotaped classroom enactment of those tasks, and pre-post test student achievement from the Baseline and final year of the study. Results indicate that, on average, teachers increased on all abilities during the study and changes were statistically significant for interpreting students ideas, eliciting questions, and feedback. HLM models showed that while only the quality of feedback was a significant predictor at Baseline, it was teachers' task design and interpretation of ideas in Year 3. These results suggest the efficacy of the FADC in supporting teachers' formative assessment abilities. Findings are interpreted in light of professional development and formative assessment literatures.

AbstractIn this study, we explore the application of process mining techniques on assessment log data to explore problem-solving strategies in Algebra. By analyzing sequences of student activities, we demonstrate the significant potential of process mining in identifying problem-solving strategies that lead to successful and unsuccessful outcomes. Our findings reveal that students who successfully solve the problem tend to follow one of three structured strategies, displaying a systematic process in filling the boxes of a Pascal’s triangle. Conversely, those who falter often start with a correct strategy but deviate by inserting incorrect values, especially in central boxes. Further analysis provides insights into the strategies and potential misconceptions among students from various disability groups. Notably, autistic students exhibit unique patterns, such as initiating the solution from the triangle’s right side, contrary to the common left-to-right strategy, and consistently applying this approach even when errors occur. [ABSTRACT FROM AUTHOR]