The Special Education Referral And Evaluation Process For English Language Learners

Research shows that there is a disproportionate number of English Language Learners (ELLs) in special education. The over and under representation of this population in special education can be linked back to issues within the referral and evaluation process. This project looks to answer the question: What accommodations need to be made to the special education process to ensure that English Language Learners are appropriately referred and evaluated? This project resulted in a flowchart for each step in the special education referral and evaluation process. Research- based practices are presented for each step of the process. The flowchart was created to be used by special education and general education teachers in any school district. School districts have varying, and at times vague, guidelines for assessing ELLs for special education. The goal of this project is to create an outline to aid in the process of implementing best practices, so that ELLs are appropriately being identified for special education and eventually eliminate the disproportionality amongst ELLs in special education. (167 words

Alliance, power dynamics and response styles in the clinical supervisory relationship

Supervisors provide evaluations, act as professional gatekeepers, and serve a central function to psychotherapy training, yet little is known about the impact of power dynamics on the supervisory relationship and the psychotherapy trainee. The present study offers a closer examination of Bordin’s (1983) concept of supervisory alliance, revealing a largely unappreciated emphasis on defusing tension associated with power. This study is the first to investigate how soft and hard power impact supervisory alliance, while also further validating the recently developed Power Dynamics in Supervision Scale (PDSS; Cook, McKibben & Wind, 2018). The Core Conflictual Relationship Theme (Luborsky & Crits-Christoph, 1998) enriched results with qualitative investigation of supervisors’ response style. A diverse sample of N = 311 psychotherapy trainees and a subset of 20 matched pairs of trainees and supervisors completed an online self-report survey. A model involving hard power and positive response style by supervisors predicted 56% of variance in trainee-rated supervisory alliance, and a model involving soft power and negative response style by supervisors predicted 51% of variance in alliance. Supervisor use of soft power appeared to serve as a buffer to trainees’ perceived negative response styles by supervisors. Whether supervisors explicitly established goals at the outset of supervision predicted 13% of supervisory alliance and 6% of perceived balance of power. Concurrent validity testing supported the construction of the PDSS. Far fewer matched pair supervisory dyads were recruited than aimed for, but in the matched pair subsample, small, non-significant associations were found between trainee and supervisor ratings of alliance, hard and soft power, and the PDSS. Implications of the results for deepening the quality of supervisor-trainee dyads are discussed

On time-reckoning in old Saami culture

Time-reckoning in old Saami culture was an orientation towards macrocosmos and microcosmos, conditioned by factors in the ecological environment affecting the course of man's practical activity. In a society or a culture where hunting and fishing and breeding animals were the chief occupations, the concepts were influenced by the circumstances surrounding these activities. The concepts of time and time-reckoning were connected with a special content—winter and summer—caused by the biocosmic rhythm. Fauna and flora gave the indications of the coming season. Among these the bear' s hibernation was a stable and prominent sign, due to his extreme sensitivity towards the biocosmic rhythm. We can call this an ecological measuring of time, well-fitting to the different occupations of a hunter, his settlement and life-style of the main seasons. As the bear was linked to the points of time when light and darkness were shifting, the bear became an important factor in old Saami culture for the orientation on both macrocosmic and microcosmic level

“Hay Sacks Anonymous”: Living in the Shadow of the Unidentified. Psychological Aspect s of Physical Inactivity from a Phenomenological Perspective

The present qualitative study emanates from a phenomenological perspective and has the purpose of creating an understanding for what a so-called “hay sack” is as well as understanding the experiences of a hay sack. In this context a hay sack refers to a person with low physical activity. Eight hay sacks between 36-58 years of age were interviewed about their experiences. Karlsson’s (1995) EPP-method was used. The analysis resulted in 13 categories. A hay sack wants to, but is unable to engage in regular physical activity as a consequence of something unidentified, possibly a psychological barrier. Being a hay sack involves thoughts and feelings which are expressed in a variety of ways such as excuses and anxiety about future health

Transforming Unstructured Text into Data with Context Rule Assisted Machine Learning (CRAML)

We describe a method and new no-code software tools enabling domain experts to build custom structured, labeled datasets from the unstructured text of documents and build niche machine learning text classification models traceable to expert-written rules. The Context Rule Assisted Machine Learning (CRAML) method allows accurate and reproducible labeling of massive volumes of unstructured text. CRAML enables domain experts to access uncommon constructs buried within a document corpus, and avoids limitations of current computational approaches that often lack context, transparency, and interpetability. In this research methods paper, we present three use cases for CRAML: we analyze recent management literature that draws from text data, describe and release new machine learning models from an analysis of proprietary job advertisement text, and present findings of social and economic interest from a public corpus of franchise documents. CRAML produces document-level coded tabular datasets that can be used for quantitative academic research, and allows qualitative researchers to scale niche classification schemes over massive text data. CRAML is a low-resource, flexible, and scalable methodology for building training data for supervised ML. We make available as open-source resources: the software, job advertisement text classifiers, a novel corpus of franchise documents, and a fully replicable start-to-finish trained example in the context of no poach clauses

Creating Data from Unstructured Text with Context Rule Assisted Machine Learning (CRAML)

Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that builds structured, labeled datasets which are accurate and reproducible. CRAML enables domain experts to access uncommon constructs within a document corpus in a low-resource, transparent, and flexible manner. CRAML produces document-level datasets for quantitative research and makes qualitative classification schemes scalable over large volumes of text. We demonstrate that the method is useful for bibliographic analysis, transparent analysis of proprietary data, and expert classification of any documents with any scheme. To demonstrate this process for building data from text with Machine Learning, we publish open-source resources: the software, a new public document corpus, and a replicable analysis to build an interpretable classifier of suspected “no poach” clauses in franchise documents

Latent space conditioning for improved classification and anomaly detection

We propose a new type of variational autoencoder to perform improved pre-processing for clustering and anomaly detection on data with a given label. Anomalies however are not known or labeled. We call our method conditional latent space variational autonencoder since it separates the latent space by conditioning on information within the data. The method fits one prior distribution to each class in the dataset, effectively expanding the prior distribution to include a Gaussian mixture model. Our approach is compared against the capabilities of a typical variational autoencoder by measuring their V-score during cluster formation with respect to the k-means and EM algorithms. For anomaly detection, we use a new metric composed of the mass-volume and excess-mass curves which can work in an unsupervised setting. We compare the results between established methods such as as isolation forest, local outlier factor and one-class support vector machine

Symbolic Behavior in Regular Classrooms: A Specification of Symbolic and Non-Symbolic Behavior

Students’ capabilities to use symbolic information in classroom setting could be expected to influence their possibilities to be active and participating. The development of strategies for teachers to compensate for reduced capability need specific operational definition of symbolic behavior. Fifty-three students, aged 11–13 years old, 29 boys and 24 girls, from three classes in the same Swedish compulsory regular school participated in the current study. After a short training sequence 25 students (47%) were defined as showing symbolic behavior (symbolic), and 28 students (53%) were not (non-symbolic), based on their follow-up test performances. Symbolic and non-symbolic differed significantly on post-test performances (p < 0.05). Surprisingly, non-symbolic behavior deteriorated their performance, while symbolic enhanced their performance (p < 0.05). The results indicate that the operational definition used in the present study may be useful in further studies relating the capability to show symbolic behavior and students’ activity and participation in classroom settings