Learning with self-generated drawings and the impact of learners’ emotional states

IntroductionThe present study examined the influence of emotional states when learning with self-generated drawings. It was assumed that learners in a positive emotional state would profit from learning with self-generated drawings, while learners in a negative emotional state would not profit from this strategy to the same extent but would rather benefit through reading.MethodsUniversity students (N  =  123) were randomly assigned to one of four conditions resulting from a 2  ×  2 design with self-generated drawings (yes vs. no) and emotional state (positive vs. negative) as independent variables.ResultsResults showed that learning with self-generated drawings was more beneficial for a following transfer test than learning without drawings – irrespective of a learner’s emotional state. The quality of self-generated drawings predicted the learning outcomes of the retention and pictorial test, but not for transfer.DiscussionMissing effects of emotional states and the missing interaction with self-generated drawings will be discussed

Detecting Concept Drift With Neural Network Model Uncertainty

Deployed machine learning models are confronted with the problem of changing data over time, a phenomenon also called concept drift. While existing approaches of concept drift detection already show convincing results, they require true labels as a prerequisite for successful drift detection. Especially in many real-world application scenarios-like the ones covered in this work-true labels are scarce, and their acquisition is expensive. Therefore, we introduce a new algorithm for drift detection, Uncertainty Drift Detection (UDD), which is able to detect drifts without access to true labels. Our approach is based on the uncertainty estimates provided by a deep neural network in combination with Monte Carlo Dropout. Structural changes over time are detected by applying the ADWIN technique on the uncertainty estimates, and detected drifts trigger a retraining of the prediction model. In contrast to input data-based drift detection, our approach considers the effects of the current input data on the properties of the prediction model rather than detecting change on the input data only (which can lead to unnecessary retrainings). We show that UDD outperforms other state-of-the-art strategies on two synthetic as well as ten real-world data sets for both regression and classification tasks

Is learning with elaborative interrogation less desirable when learners are depleted?

When learning with elaborative interrogation, learners are requested by means of prompts to generate parts of the study material. There is evidence, that learning with elaborative interrogation is beneficial. However, it is conceivable that for elaborative interrogation to be beneficial for learning, learners also need resources available to be able to correctly generate parts of the study material. In this connection, one potentially important factor for successfully carrying out such effortful analytic processes, like generating information, is cognitive self-control. However, self-control seems to be a limited resource that can be depleted. Hence, under conditions of depleted self-regulatory resources (ego depletion), elaborative interrogation might lead to an incomplete generation of the requested information, resulting in incomplete study material. Thus, elaborative interrogation may be only beneficial under nondepleted conditions, but disadvantageous under depleted conditions. To investigate this, 97 persons were randomly assigned to one of four conditions resulting from a 2 × 2 between-subjects design with the independent variables ego depletion (yes vs. no) and learning condition (elaborative interrogation vs. control). Ego depletion was manipulated with a writing task: Participants were instructed to transcribe a text on a blank sheet, but only participants in the depletion condition were instructed to omit the letters e and n wherever they would normally appear in their writing. For the elaborative interrogation condition, some segments of the regular text were removed and prompts asking for that particular information were provided. For the control condition, the regular text was provided while no prompts were given. The main dependent variables were the learning outcome measures of a retention test and a transfer test. 2 × 2-ANCOVAs showed no effects of ego depletion, no effects of learning condition and no interaction between ego depletion and learning condition – neither for retention nor for transfer. The concept of ego depletion is recently discussed controversy and these results do contribute to the skeptical view that queries the impact of the concept of ego depletion – at least for cognitive tasks. Moreover, these results question whether elaborative interrogation are also desirable when assessing learning outcomes by means of retention and transfer tests

Why the cells look like that - the influence of learning with emotional design and elaborative interrogations

We investigated emotional design features that may influence multimedia learning with a self-generated learning (SGL) activity, namely answering elaborative interrogations. We assumed that a positive emotional design would be associated with a higher motivation to accomplish the additional SGL activity. Moreover, an interaction was expected: Learners learning with a positive emotional design should profit from learning with elaborative interrogations whereas learners learning with a negative emotional design would not profit from this strategy to the same extent but would rather benefit through reading. Since no negative emotional design existed yet, we additionally took the challenge to construct one. In a preliminary study, the emotional design features were pre-tested for their influence on emotional state and according to evaluation results, emotional design features were modified for the final versions. For the main study, German students (N = 228) were randomly assigned to one of six conditions that resulted from a 3 × 2 Design with emotional design (intended-positive vs. intended-neutral vs. intended-negative) and SGL activity (elaborative interrogations vs. no elaborative interrogations). Contrary to expectations, the intended-negative design worked not out as intended, but was rather comparable with the positive emotional design with respect to learners’ emotional states. Learner motivation was higher when learning with the intended-negative emotional than the neutral design. The quality of the elaborated answers and learner motivation correlated positively with the performance of all learning outcome scores. For transfer questions which addressed the elaborated concepts, an interaction can be reported: learners learning with the positive emotional design benefitted from learning by reading compared to answering the elaborative interrogations. Regarding transfer questions whose concepts were explicitly described in the instructional material, it was better to learn with the intended-negative emotional than the neutral design. According to results of mediation analyses, the influence of motivation on learning outcomes could mostly be explained by the influence of motivation on answering the elaborative interrogations. Implications for creating emotional design as well as its effect on learning are discussed

Extrusion-based 3D printing of osteoinductive scaffolds with a spongiosa-inspired structure

Critical-sized bone defects resulting from trauma, inflammation, and tumor resections are individual in their size and shape. Implants for the treatment of such defects have to consider biomechanical and biomedical factors, as well as the individual conditions within the implantation site. In this context, 3D printing technologies offer new possibilities to design and produce patient-specific implants reflecting the outer shape and internal structure of the replaced bone tissue. The selection or modification of materials used in 3D printing enables the adaption of the implant, by enhancing the osteoinductive or biomechanical properties. In this study, scaffolds with bone spongiosa-inspired structure for extrusion-based 3D printing were generated. The computer aided design process resulted in an up scaled and simplified version of the bone spongiosa. To enhance the osteoinductive properties of the 3D printed construct, polycaprolactone (PCL) was combined with 20% (wt) calcium phosphate nano powder (CaP). The implants were designed in form of a ring structure and revealed an irregular and interconnected porous structure with a calculated porosity of 35.2% and a compression strength within the range of the natural cancellous bone. The implants were assessed in terms of biocompatibility and osteoinductivity using the osteosarcoma cell line MG63 and patient-derived mesenchymal stem cells in selected experiments. Cell growth and differentiation over 14 days were monitored using confocal laser scanning microscopy, scanning electron microscopy, deoxyribonucleic acid (DNA) quantification, gene expression analysis, and quantitative assessment of calcification. MG63 cells and human mesenchymal stem cells (hMSC) adhered to the printed implants and revealed a typical elongated morphology as indicated by microscopy. Using DNA quantification, no differences for PCL or PCL-CaP in the initial adhesion of MG63 cells were observed, while the PCL-based scaffolds favored cell proliferation in the early phases of culture up to 7 days. In contrast, on PCL-CaP, cell proliferation for MG63 cells was not evident, while data from PCR and the levels of calcification, or alkaline phosphatase activity, indicated osteogenic differentiation within the PCL-CaP constructs over time. For hMSC, the highest levels in the total calcium content were observed for the PCL-CaP constructs, thus underlining the osteoinductive properties