Predicting intraindividual changes in learning strategies: The effects of previous achievement

Socio-cognitive models of self-regulated learning (e.g., Pintrich, 2000) emphasize contextualized nature oflearning process, and within-person variation in learning processes, along with between-person variability in selfregulation.Previous studies about contextual nature of learning strategies have mostly focused on the effects ofdifferent contextual factors on interindividual differences in learning strategies utilization. However, less attentionwas given to the question about contextual effects on within-person variability in learning strategies. In this paper,the following questions were explored: (a) whether students exhibit intraindividual variability in learning strategiesbetween two measurement occasions, or not, and (b) to what degree the observed intraindividual variability in selectedlearning strategies between two learning episodes can be accounted for by achievement after the first learningepisode. The research questions were analyzed under the methodological framework of the latent state-trait theory(Steyer, Ferring, & Schmitt, 1992). The sample consisted of 297 first year university students attending Introductionto Psychology course. Selected learning strategies (organization, elaboration, and critical reasoning) were measuredby means of adapted version of the Inventar zur Erfassung von Lernstrategien im Studium (Wild & Schiefele, 1994).Participants filled in the questionnaire before the exams on two occasions with a 7-week time lag. Students’ scoreson the first exam were obtained from the teacher’s record. Results provide the evidence that there are individual differencesin students’ changes in the frequency of use of learning strategies at the end of semester (compared to themidsemester). Also, students who scored higher at the first exam exhibited less intraindividual variability in learningstrategies utilization

Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions

Convolutional Neural Networks (CNN) have demonstrated their capabilities on the agronomical field, especially for plant visual symptoms assessment. As these models grow both in the number of training images and in the number of supported crops and diseases, there exist the dichotomy of (1) generating smaller models for specific crop or, (2) to generate a unique multi-crop model in a much more complex task (especially at early disease stages) but with the benefit of the entire multiple crop image dataset variability to enrich image feature description learning. In this work we first introduce a challenging dataset of more than one hundred-thousand images taken by cell phone in real field wild conditions. This dataset contains almost equally distributed disease stages of seventeen diseases and five crops (wheat, barley, corn, rice and rape-seed) where several diseases can be present on the same picture. When applying existing state of the art deep neural network methods to validate the two hypothesised approaches, we obtained a balanced accuracy (BAC=0.92) when generating the smaller crop specific models and a balanced accuracy (BAC=0.93) when generating a single multi-crop model. In this work, we propose three different CNN architectures that incorporate contextual non-image meta-data such as crop information onto an image based Convolutional Neural Network. This combines the advantages of simultaneously learning from the entire multi-crop dataset while reducing the complexity of the disease classification tasks. The crop-conditional plant disease classification network that incorporates the contextual information by concatenation at the embedding vector level obtains a balanced accuracy of 0.98 improving all previous methods and removing 71% of the miss-classifications of the former methods

Context effects on second-language learning of tonal contrasts.

Studies of lexical tone  learning generally focus on monosyllabic contexts, while reports of phonetic learning benefits associated with input variability are based largely on experienced learners. This study trained inexperienced learners on Mandarin tonal contrasts to test two hypotheses regarding the influence of context and variability on tone  learning. The first hypothesis was that increased phonetic variability of tones in disyllabic contexts makes initial tone  learning more challenging in disyllabic than monosyllabic words. The second hypothesis was that the learnability of a given tone varies across contexts due to differences in tonal variability. Results of a word learning experiment supported both hypotheses: tones were acquired less successfully in disyllables than in monosyllables, and the relative difficulty of disyllables was closely related to contextual tonal variability. These results indicate limited relevance of monosyllable-based data on Mandarin learning for the disyllabic majority of the Mandarin lexicon. Furthermore, in the short term, variability can diminish learning; its effects are not necessarily beneficial but dependent on acquisition stage and other learner characteristics. These findings thus highlight the importance of considering contextual variability and the interaction between variability and type of learner in the design, interpretation, and application of research on phonetic learning

Contextual Motifs: Increasing the Utility of Motifs using Contextual Data

Motifs are a powerful tool for analyzing physiological waveform data. Standard motif methods, however, ignore important contextual information (e.g., what the patient was doing at the time the data were collected). We hypothesize that these additional contextual data could increase the utility of motifs. Thus, we propose an extension to motifs, contextual motifs, that incorporates context. Recognizing that, oftentimes, context may be unobserved or unavailable, we focus on methods to jointly infer motifs and context. Applied to both simulated and real physiological data, our proposed approach improves upon existing motif methods in terms of the discriminative utility of the discovered motifs. In particular, we discovered contextual motifs in continuous glucose monitor (CGM) data collected from patients with type 1 diabetes. Compared to their contextless counterparts, these contextual motifs led to better predictions of hypo- and hyperglycemic events. Our results suggest that even when inferred, context is useful in both a long- and short-term prediction horizon when processing and interpreting physiological waveform data.Comment: 10 pages, 7 figures, accepted for oral presentation at KDD '1

Developing Student, Family, and School Constructs From NLTS2 Data

The purpose of this study was to use data from the National Longitudinal Transition Study–2 (NLTS2) to (a) conceptually identify and empirically establish student, family, and school constructs; (b) explore the degree to which the constructs can be measured equivalently across disability groups; and (c) examine latent differences (means, variances, and correlations) in the constructs across disability groups. Conceptual analysis of NLTS2 individual survey items yielded 21 student, family, and school constructs, and 16 were empirically supported. Partial strong metric invariance was established across disability groups, and in the latent space, a complex pattern of mean and variance differences across disability groups was found. Disability group moderated the correlational relationships between multiple predictor constructs, suggesting the key role of disability-related characteristics in understanding the experiences of youth with disabilities. Implications for future research and practice are discussed