Effects of Self-Assessment on Math Homework

In this action research study of my eighth grade differentiated Algebra students, I investigated the effects of students using self-assessment on their homework. Students in my class were unmotivated and failed test objectives consistently. I wanted students to see that they controlled their learning and could be motivated to succeed. Formative assessment tells students how they need to improve. Learning needs to happen before they can be assessed. Self-assessment is one tool that helps students know if they are learning. A rubric scoring guide, daily documentation sheet and feedback on homework and test correlations were used to help students monitor their learning. Students needed time to develop the skill to self-assess. Students began to understand the relationship between homework and performing well on tests by the end of the action research period. Early in the period, most students encountered difficulty understanding that they controlled their learning and did not think homework was important. By the end of the year, all students said homework was important and that it helped them on quizzes and tests. Motivating students to complete homework is difficult. Teaching them to self-assess and to keep track of their learning helps them stay motivated.

Space–time zero-inflated count models of Harbor seals

Environmental data are spatial, temporal, and often come with many zeros. In this paper, we included space–time random effects in zero-inflated Poisson (ZIP) and ‘hurdle’ models to investigate haulout patterns of harbor seals on glacial ice. The data consisted of counts, for 18 dates on a lattice grid of samples, of harbor seals hauled out on glacial ice in Disenchantment Bay, near Yakutat, Alaska. A hurdle model is similar to a ZIP model except it does not mix zeros from the binary and count processes. Both models can be used for zero-inflated data, and we compared space–time ZIP and hurdle models in a Bayesian hierarchical model. Space–time ZIP and hurdle models were constructed by using spatial conditional autoregressive (CAR) models and temporal first-order autoregressive (AR(1)) models as random effects in ZIP and hurdle regression models. We created maps of smoothed predictions for harbor seal counts based on ice density, other covariates, and spatio-temporal random effects. For both models predictions around the edges appeared to be positively biased. The linex loss function is an asymmetric loss function that penalizes overprediction more than underprediction, and we used it to correct for prediction bias to get the best map for space–time ZIP and hurdle models.

Improving IRT parameter estimates with small sample sizes: Evaluating the efficacy of a new data augmentation technique

The 3PL model is a flexible and widely used tool in assessment. However, it suffers from limitations due to its need for large sample sizes. This study introduces and evaluates the efficacy of a new sample size augmentation technique called Duplicate, Erase, and Replace (DupER) Augmentation through a simulation study. Data are augmented using several variations of DupER Augmentation (based on different imputation methodologies, deletion rates, and duplication rates), analyzed in BILOG-MG 3, and results are compared to those obtained from analyzing the raw data. Additional manipulated variables include test length and sample size. Estimates are compared using seven different evaluative criteria. Results are mixed and inconclusive. DupER augmented data tend to result in larger root mean squared errors (RMSEs) and lower correlations between estimates and parameters for both item and ability parameters. However, some DupER variations produce estimates that are much less biased than those obtained from the raw data alone. For one DupER variation, it was found that DupER produced better results for low-ability simulees and worse results for those with high abilities. Findings, limitations, and recommendations for future studies are discussed. Specific recommendations for future studies include the application of Duper Augmentation (1) to empirical data, (2) with additional IRT models, and (3) the analysis of the efficacy of the procedure for different item and ability parameter distributions.

Correlation of the middle Eocene Kellogg Shale of northern California

The Kellogg Shale of northern California has traditionally been considered to be late Eocene in age on the basis of benthic foraminifer, radiolarian, and diatom correlations. The 30-m-thick Kellogg section exposed west of Byron, California, however, contains middle Eocene planktonic foraminifers (Zone P12), coccoliths (Subzones CP13c and CP14a), silicoflagellates (Dictyocha hexacantha Zone), and diatoms. Quantitative studies of the silicoflagellates and diatoms show a general cooling trend through the section which is consistent with paleoclimatic trends for this part of the middle Eocene (ca. 42-45 Ma) from elsewhere in the world. Seven new silicoflagellate taxa (Corbisema angularis. C, exilis, C, hastate miranda, C. inermis ballantina, C. regina, Dictyocha byronalis, Naviculopsis Americana) and one new coccolithophorid species (Helicosphaera neolophota) are described.