- James S. Kim
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James S. Kim
Longitudinal models of individual growth typically emphasize between-person predictors of change but ignore how growth may vary within persons because each person contributes only one point at each time to the model. In contrast, modeling growth with multi-item assessments allows evaluation of how relative item performance may shift over time. While traditionally viewed as a nuisance under the label of “item parameter drift” (IPD) in the Item Response Theory literature, we argue that IPD may be of substantive interest if it reflects how learning manifests on different items at different rates. In this study, we present a novel application of the Explanatory Item Response Model (EIRM) to assess IPD in a causal inference context. Simulation results show that when IPD is not accounted for, both parameter estimates and their standard errors can be affected. We illustrate with an empirical application to the persistence of transfer effects from a content literacy intervention on vocabulary knowledge, revealing how researchers can leverage IPD to achieve a more fine-grained understanding of how vocabulary learning develops over time.
This study contributes to the science of teaching reading by illustrating how a ubiquitous classroom practice – read alouds – can be enhanced by fostering teacher language practices that support students’ ability to read for understanding. This experimental study examines whether and to what extent providing structured teacher read aloud supplements in a social studies read aloud can allow students to leverage a familiar science schema and thereby positively impact reading comprehension outcomes. Treatment students received a single social studies read-aloud on the story of Apollo 11 with structured teacher read aloud supplements while control students received the same read-aloud story but without structured supplements. Effect sizes from hierarchical linear models indicated that students in the treatment condition significantly outperformed students in the control condition on four measures of domain-specific reading comprehension. Further exploratory analyses using structural equation modeling examined the extent that teacher language mediated the treatment effect. Results indicated that teachers going above and beyond the intervention script explained 67 percent of the treatment effect. Structured supplements for read alouds can help students see important connections between schemas, which ultimately aids in reading comprehension.
We leverage log data from an educational app and two-way text message records from over 3,500 students during the summers of 2019 and 2020, along with in-depth interviews in Spanish and English, to identify patterns of family engagement with educational technology. Based on the type and timing of technology use, we identify several distinct profiles of engagement, which we group into two categories: Independent Users who engage with technology-based educational software independently, and Interaction-Supported Users who use two-way communications to support their engagement. We also find that as the demands of families from schools increased during the COVID-19 pandemic, Spanish-speaking families were significantly more likely than English-speaking families to engage with educational technology across all categories of families, particularly as Interaction-Supported Users.
The current study aimed to explore the COVID-19 impact on the reading achievement growth of Grade 3-5 students in a large urban school district in the U.S. and whether the impact differed by students’ demographic characteristics and instructional modality. Specifically, using administrative data from the school district, we investigated to what extent students made gains in reading during the 2020-2021 school year relative to the pre-COVID-19 typical school year in 2018-2019. We further examined whether the effects of students’ instructional modality on reading growth varied by demographic characteristics. Overall, students had lower average reading achievement gains over the 9-month 2020-2021 school year than the 2018-2019 school year with a learning loss effect size of 0.54, 0.27, and 0.28 standard deviation unit for Grade 3, 4, and 5, respectively. Substantially reduced reading gains were observed from Grade 3 students, students from high-poverty backgrounds, English learners, and students with reading disabilities. Additionally, findings indicate that among students with similar demographic characteristics, higher-achieving students tended to choose the fully remote instruction option, while lower-achieving students appeared to opt for in-person instruction at the beginning of the 2020-2021 school year. However, students who received in-person instruction most likely demonstrated continuous growth in reading over the school year, whereas initially higher-achieving students who received remote instruction showed stagnation or decline, particularly in the spring 2021 semester. Our findings support the notion that in-person schooling during the pandemic may serve as an equalizer for lower-achieving students, particularly from historically marginalized or vulnerable student populations.
Analyses that reveal how treatment effects vary allow researchers, practitioners, and policymakers to better understand the efficacy of educational interventions. In practice, however, standard statistical methods for addressing Heterogeneous Treatment Effects (HTE) fail to address the HTE that may exist within outcome measures. In this study, we present a novel application of the Explanatory Item Response Model (EIRM) for assessing what we term “item-level” HTE (IL-HTE), in which a unique treatment effect is estimated for each item in an assessment. Results from data simulation reveal that when IL-HTE are present but ignored in the model, standard errors can be underestimated and false positive rates can increase. We then apply the EIRM to assess the impact of a literacy intervention focused on promoting transfer in reading comprehension on a digital formative assessment delivered online to approximately 8,000 third-grade students. We demonstrate that allowing for IL-HTE can reveal treatment effects at the item-level masked by a null average treatment effect, and the EIRM can thus provide fine-grained information for researchers and policymakers on the potentially heterogeneous causal effects of educational interventions.
The current study replicated and extended the previous findings of content-integrated literacy intervention focusing on its effectiveness on first- and second-grade English learners’ (N = 1,314) reading comprehension, writing, vocabulary knowledge, and oral proficiency. Statistically significant findings were replicated on science and social studies vocabulary knowledge (ES = .51 and .53, respectively) and argumentative writing (ES = .27 and .41, respectively). Furthermore, treatment group outperformed control group on reading (ES = .08) and listening comprehension (ES = .14). Vocabulary knowledge and oral proficiency mediated treatment effects on reading comprehension, whereas only oral proficiency mediated effects on writing. Findings replicate main effects on vocabulary knowledge and writing, while also extending previous research by highlighting mechanisms underlying improved reading comprehension and writing.
In a randomized trial that collects text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by human raters. An impact analysis can then be conducted to compare treatment and control groups, using the hand-coded scores as a measured outcome. This process is both time and labor-intensive, which creates a persistent barrier for large-scale assessments of text. Furthermore, enriching ones understanding of a found impact on text outcomes via secondary analyses can be difficult without additional scoring efforts. Machine-based text analytic and data mining tools offer one potential avenue to help facilitate research in this domain. For instance, we could augment a traditional impact analysis that examines a single human-coded outcome with a suite of automatically generated secondary outcomes. By analyzing impacts across a wide array of text-based features, we can then explore what an overall change signifies, in terms of how the text has evolved due to treatment. In this paper, we propose several different methods for supplementary analysis in this spirit. We then present a case study of using these methods to enrich an evaluation of a classroom intervention on young children’s writing. We argue that our rich array of findings move us from “it worked” to “it worked because” by revealing how observed improvements in writing were likely due, in part, to the students having learned to marshal evidence and speak with more authority. Relying exclusively on human scoring, by contrast, is a lost opportunity.
Parental text messaging interventions are growing in popularity to encourage at-home reading, school-attendance, and other educational behaviors. These interventions, which often combine multiple components, frequently demonstrate varying amounts of effectiveness, and researchers often cannot determine how individual components work alone or in combination with one another. Using a 2x2x3 factorial experiment, we investigate the effects of individual and interacted components from three behavioral levers to support summer reading: providing updated, personalized information; emphasizing different reading views; and goal setting. We find that the personalized information condition scored on average 0.03 SD higher on fall reading assessments. Texting effects on test scores were enhanced by messages that emphasized reading being useful for both entertainment and building skills compared to skill building alone or entertainment alone. These results continue to build our understanding that while text message can be an effective tool for parent engagement, the specific content of the message can lead to meaningful differences in the magnitude of the effects.