Practitioner Inquiry: Teaching literacy with English language learners

This qualitative research examines a practitioner inquiry group comprised of teachers of English Language Learners (ELLs) with the following research questions in mind: What happens when a group of ESL teachers collaborate in a dialogically inspired professional development context to learn about navigating discussion with complex texts and their ELL students? How does teacher learning evolve and address the complexities of the teacher/learner discourse under discussion in the professional development inquiry? What are the thematic and discursive contours of teaching and learning in this professional development context? In what ways will lesson development be relevant to the needs of those in the practitioners’ settings? This study documents the concerns, strengths, and weaknesses that ELL teachers express about teaching ELLs’ literacy through an examination of teachers’ experiences over eight months of group sessions. Dialogic teaching is presented in the inquiry as a lens to compare and contrast teachers’ ideas about their engagement with ELLs in comprehending complex texts in literacy learning. Data sources include (1) digital recordings of initial interviews, (2) field notes and digital recordings of group meetings, (3) digital recordings of exit focus group, (4) transcripts of observed lessons, (5) digital recordings of debriefing interviews after observations, (6) a case study of two teachers in their classrooms, and (7) the researcher’s reflexive journal. Case studies of two teachers include additional classroom observations and in-depth interviews. Data analysis tools included narrative structure (Gee, 2011; Labov & Waletzky, 1987), critical discourse analysis (Fairclough, 1992; Rogers 2011), and grounded theory techniques (Strauss & Corbin, 2008). Findings show that practitioners’ discourse changed to include more positive appraisals of their students’ classroom discussions after working through readings about dialogic teaching (Alexander, 2008; Boyd & Markarian, 2011; Reznitskaya, 2012; Wells, 2002). An awareness of how EL students are positioned in higher education is revealed with an understanding of the complex nuances of English language practitioner discourse. This research adds to existing scholarship in professional development for English language teachers and in-service teachers as well as to narratives about teaching literacy with ELLs

Supervised clustering of streaming data for email batch detection

We address the problem of detecting batches of emails that have been created according to the same template. This problem is motivated by the desire to filter spam more effectively by exploiting collective information about entire batches of jointly generated messages. The application matches the problem setting of supervised clustering, because examples of correct clusterings can be collected. Known decoding procedures for supervised clustering are cubic in the number of instances. When decisions cannot be reconsidered once they have been made – owing to the streaming nature of the data – then the decoding problem can be solved in linear time. We devise a sequential decoding procedure and derive the corresponding optimization problem of supervised clustering. We study the impact of collective attributes of email batches on the effectiveness of recognizing spam emails. 1

Adaptive item selection under matroid constraints

The shadow testing approach (STA; van der Linden & Reese, 1998) is considered the state of the art in constrained item selection for computerized adaptive tests. The present paper shows that certain types of constraints (e.g., bounds on categorical item attributes) induce a matroid on the item bank. This observation is used to devise item selection algorithms that are based on matroid optimization and lead to optimal tests, as the STA does. In particular, a single matroid constraint can be treated optimally by an efficient greedy algorithm that selects the most informative item preserving the integrity of the constraints. A simulation study shows that for applicable constraints, the optimal algorithms realize a decrease in standard error (SE) corresponding to a reduction in test length of up to 10% compared to the maximum priority index (Cheng & Chang, 2009) and up to 30% compared to Kingsbury and Zara\u27s (1991) constrained computerized adaptive testing

Simultaneous constrained adaptive item selection for group-based testing

By tailoring test forms to the test-taker\u27s proficiency, Computerized Adaptive Testing (CAT) enables substantial increases in testing efficiency over fixed forms testing. When used for formative assessment, the alignment of task difficulty with proficiency increases the chance that teachers can derive useful feedback from assessment data. The application of CAT to formative assessment in the classroom, however, is hindered by the large number of different items used for the whole class; the required familiarization with a large number of test items puts a significant burden on teachers. An improved CAT procedure for group-based testing is presented, which uses simultaneous automated test assembly to impose a limit on the number of items used per group. The proposed linear model for simultaneous adaptive item selection allows for full adaptivity and the accommodation of constraints on test content. The effectiveness of the group-based CAT is demonstrated with real-world items in a simulated adaptive test of 3,000 groups of test-takers, under different assumptions on group composition. Results show that the group-based CAT maintained the efficiency of CAT, while a reduction in the number of used items by one half to two-thirds was achieved, depending on the within-group variance of proficiencies. (DIPF/Orig.

Joint Optimization of an Autoencoder for Clustering and Embedding

Incorporating k-means-like clustering techniques into (deep) autoencoders constitutes an interesting idea as the clustering may exploit the learned similarities in the embedding to compute a non-linear grouping of data at-hand. Unfortunately, the resulting contributions are often limited by ad-hoc choices, decoupled optimization problems and other issues. We present a theoretically-driven deep clustering approach that does not suffer from these limitations and allows for joint optimization of clustering and embedding. The network in its simplest form is derived from a Gaussian mixture model and can be incorporated seamlessly into deep autoencoders for state-of-the-art performance

Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia

Event-based models (EBM) are a class of disease progression models that can be used to estimate temporal ordering of neuropathological changes from cross-sectional data. Current EBMs only handle scalar biomarkers, such as regional volumes, as inputs. However, regional aggregates are a crude summary of the underlying high-resolution images, potentially limiting the accuracy of EBM. Therefore, we propose a novel method that exploits high-dimensional voxel-wise imaging biomarkers: n-dimensional discriminative EBM (nDEBM). nDEBM is based on an insight that mixture modeling, which is a key element of conventional EBMs, can be replaced by a more scalable semi-supervised support vector machine (SVM) approach. This SVM is used to estimate the degree of abnormality of each region which is then used to obtain subject-specific disease progression patterns. These patterns are in turn used for estimating the mean ordering by fitting a generalized Mallows model. In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers' Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions. SImBioTE trains variational auto-encoders (VAE) in different brain regions independently to simulate images at varying stages of disease progression. We also validate nDEBM clinically using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In both experiments, nDEBM using high-dimensional features gave better performance than state-of-the-art EBM methods using regional volume biomarkers. This suggests that nDEBM is a promising approach for disease progression modeling.Comment: IPMI 201

Evaluating one-shot tournament predictions

We introduce the Tournament Rank Probability Score (TRPS) as a measure to evaluate and compare pre-tournament predictions, where predictions of the full tournament results are required to be available before the tournament begins. The TRPS handles partial ranking of teams, gives credit to predictions that are only slightly wrong, and can be modified with weights to stress the importance of particular features of the tournament prediction. Thus, the Tournament Rank Prediction Score is more flexible than the commonly preferred log loss score for such tasks. In addition, we show how predictions from historic tournaments can be optimally combined into ensemble predictions in order to maximize the TRPS for a new tournament