Search

Thousands of educational apps are available to students, teachers, and parents, yet research on their effectiveness is limited. This meta-analysis synthesized findings from 36 intervention studies and 285 effect sizes evaluating the effectiveness of educational apps for preschool to Grade 3 children and the moderating role of methodological, participant, and intervention characteristics. Using random effects meta-regression with robust variance estimation, we summarized the overall impact of educational apps and examined potential moderator effects. First, results from rigorous experimental and quasi-experimental studies yielded a mean weighted effect size of +0.31 standard deviations on overall achievement and comparable effects in both math and literacy. Second, the positive overall effect masks substantial variability in app effectiveness, as meta-regression analyses revealed three significant moderators of treatment effects. Treatment effects were larger for studies involving preschool rather than K-3 students, for studies using researcher-developed rather than standardized outcomes, and for studies measuring constrained rather than unconstrained skills.

The current study aimed to explore the COVID-19 impact on reading achievement growth by 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 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.

We developed a sustained content literacy intervention that emphasized building domain and topic knowledge from Grade 1 to Grade 2 and evaluated transfer effects on students' reading comprehension outcomes. The Model of Reading Engagement (MORE) intervention emphasizes thematic lessons that provide an intellectual framework for helping students connect new learning to a general schema (i.e., how scientists study past events). A total of 30 elementary schools (N = 2,952 students; N = 144 teachers) were randomly assigned to a treatment or control group. Over 12 months, the treatment group students participated in (a) spring Grade 1 thematic content literacy lessons in science and social studies followed by wide reading of thematically related informational texts during summer, and (b) fall to spring Grade 2 thematic content literacy lessons in science. After implementation of Grade 1 thematic content literacy lessons and summer support for reading, treatment group students experienced smaller summer losses on a domain-general measure of reading than control group students. Following the sustained implementation of thematic content literacy lessons in science through Grade 2, treatment group students also outperformed their control group peers on a science content reading comprehension outcome (ES = 0.18). Furthermore, we found transfer effects on science content reading comprehension that varied by passage-item type (near-, mid-, and far-transfer passages determined by the inclusion and number of directly taught words in passages). A sustained content literacy intervention that aligns content and instruction across grades can help students transfer knowledge to novel reading comprehension tasks.

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 is 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 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.

Longitudinal models typically emphasize between-person predictors of change but ignore how growth varies within persons because each person contributes only one data point at each time. 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), IPD may be of substantive interest if it reflects how learning manifests on different items or subscales at different rates. In this study, we apply the Explanatory Item Response Model to assess IPD in a causal inference context. Simulation results show that when IPD is not accounted for, both parameter estimates and standard errors can be affected. We illustrate with an empirical application to the persistence of transfer effects from a content literacy intervention , revealing how researchers can leverage IPD to achieve a more fine-grained understanding of how vocabulary learning develops over time. [ABSTRACT FROM AUTHOR]

We investigated the effectiveness of a sustained and spiraled content literacy intervention that emphasizes building domain and topic knowledge schemas and vocabulary for elementary-grade students. The model of reading engagement intervention underscores thematic lessons that provide an intellectual structure for helping students connect new learning to a general schema in Grade 1 (animal survival), Grade 2 (scientific investigation of past events like dinosaur mass extinctions), and Grade 3 (scientific investigation of living systems). A total of 30 elementary schools (N = 2,870 students) were randomized to a treatment or control condition. In the treatment condition (i.e., full spiral curriculum), students participated in content literacy lessons from Grades 1 to 3 during the school year and wide reading of thematically related informational texts in the summer following Grades 1 and 2. In the control condition (i.e., partial spiral curriculum), students participated in lessons in only Grade 3. The Grade 3 lessons for both conditions were implemented online during the COVID-19 pandemic school year. Results reveal that treatment students outperformed control students on science vocabulary knowledge across all three grades. Furthermore, intent-to-treat analyses revealed positive transfer effects on Grade 3 science reading (ES =.14), domain-general reading comprehension (ES =.11), and mathematics achievement (ES =.12). Treatment impacts were sustained at 14-month follow-up on Grade 4 reading comprehension (ES =.12) and mathematics achievement (ES =.16). Findings indicate that a content literacy intervention that spirals topics and vocabulary across grades can improve students' long-term academic achievement outcomes. Public Significance Statement: This experimental study illustrates how sustaining and spiraling science schemas (background knowledge) and vocabulary from Grades 1 to 3 can improve students' ability to comprehend passages in science, English language arts, and mathematics. Furthermore, findings suggest that systematically building background and vocabulary knowledge can sustain positive gains in elementary-grade students' reading comprehension ability through the end of Grade 4, 14 months after the conclusion of the intervention activities. [ABSTRACT FROM AUTHOR]

Selective removal of alkali metal cations from mixed cation multiply-charged peptide ions is demonstrated here using gas-phase ion/ion reactions with a series of weakly coordinating anions (WCAs), including hexafluorophosphate (PF6 (-)), tetrakis[3,5-bis(trifluoromethyl)phenyl]borate (BARF), tetrakis(pentafluorophenyl)borate (TPPB), and carborane (CHB11Cl11 (-)). In all cases, a long-lived complex is generated by dication/anion condensation followed by ion activation to compare proton transfer with alkali ion transfer from the peptide to the anion. The carborane anion was the only anion studied to undergo dissociation exclusively through loss of the metallated anion, regardless of the studied metal adduct. All other anions studied yield varying abundances of protonated and metallated peptide depending on the peptide sequence and the metal identity. Density functional theory calculations suggest that for the WCAs studied, metal ion transfer is most strongly favored thermodynamically, which is consistent with the experimental results. The carborane anion is demonstrated to be a robust reagent for the selective removal of alkali metal cations from peptide cations with mixtures of excess protons and metal cations.