Towards teaching analytics: a contextual model for analysis of students' evaluation of teaching through text mining and machine learning classification.

Recent trends in educational technology have led to emergence of methods such as teaching analytics (TA) in understanding and management of the teaching-learning processes. Didactically, teaching analytics is one of the promising and emerging methods within the Education domain that have proved to be useful, towards scholastic ways to make use of substantial pieces of evidence drawn from educational data to improve the teaching-learning processes and quality of performance. For this purpose, this study proposed an educational process and data mining plus machine learning (EPDM + ML) model applied to contextually analyze the teachers' performances and recommendations based on data derived from students' evaluation of teaching (SET). The EPDM + ML model was designed and implemented based on amalgamation of the Text mining and Machine learning technologies that builds on the descriptive decision theory, which studies the rationality behind decisions the learners are disposed to make based on the textual data quantification and statistical analysis. To this effect, the study determines pedagogical factors that influences the students' recommendations for their teachers, what role the sentiment and emotions expressed by the students in the SET play in the way they evaluate the teachers by taking into account the gender of the teachers. This includes how to automatically predict what a student's recommendation for the teachers may be based on information about the students' gender, average sentiment, and emotional valence they have shown in the SET. Practically, we applied the Text mining technique to extract the different sentiments and emotions (intensities of the comments) expressed by the students in the SET, and then utilized the quantified data (average sentiment and emotional valence) to conduct an analysis of covariance and Kruskal Wallis Test to determine the influential factors, as well as, how the students' recommendation for the teachers differ by considering the gender constructs, respectively. While a large proportion of the comments that we analyzed (n = 85,378) was classified to be neutral and predominantly interpreted to be positive in nature considering the sentiments (76.4%), and emotional valence (88.2%) expressed by the students. The results of our analysis shows that for the students' comments which contain some kind of positive or negative sentiment (23.6%) and emotional valence (11.8%); that females students recommended the teachers taking into account the sentiments (p = .000). While the males appear to be slightly borderline in terms of emotions (p = .056) and sentiment (p = .077). Also, the EPDM + ML model showed to be a good predictor and efficient method in determining what the students' recommendation scores for the teachers would be, going by the high and acceptable values of the precision (1.00), recall (1.00), specificity (1.00), accuracy (1.00), F1-score (1.00) and zero error-rate (0.00) which we validated using the k-fold cross-validation method, with 63.6% of optimal k-values observed. In theory, we note that not only does the proposed method (EPDM + ML) proves to be useful towards effective analysis of SET and its implications within the educational domain. But can be utilized to determine prominent factors that influences the students' evaluation and recommendation of the teachers, as well as helps provide solutions to the ever-increasingly need to advance and support the teaching-learning processes and/or students' learning experiences in a rapidly changing educational environment or ecosystem.

The impact of the COVID-19 pandemic on students' feelings at high school, undergraduate, and postgraduate levels.

The COVID-19 pandemic and the enforced restrictions have harshly affected educational sectors in 161 countries around the world. With more than 1.6 billion students away from normal school life, the crisis threatens the teaching and learning processes and the students' emotional health. Herein, we present the result of a careful assessment of the feelings of over 13,000 students at high school, undergraduate, and postgraduate levels across 36 campuses over 8 subsequent weeks from the onset of the COVID-19 pandemic. The results indicate a general low energy level and dominance of negative feelings among the students regardless of their academic levels. We have recorded 5 responses (being anxious, stressed, overwhelmed, tired, and depressed) as the most frequently reported feelings in the time of lockdown. Overall, 14% of those who have reported to suffer from these feelings have also identified a need for professional help in managing their feelings throughout the quarantine period. The current study also presents several strategies to combat the undesirable consequences of COVID-19 pandemic.

Does professors' gender impact how students evaluate their teaching and the recommendations for the best professor?

This study examined the impact of the professors' gender according to a student evaluation of teaching (SET) in a private university. The study took place in a private university (n = 103,833) on six different campuses in the north region of Mexico. The distribution of the professors' gender was analyzed according to semesters, campuses, and schools. Our findings suggested that when undergraduates evaluated their professors on specific criteria concerning teaching performance, they expressed their opinion regardless of the professors' gender. However, when being asked for a single overall evaluation, as whether they would recommend the professor as one of their best professors, the students tended to favor male professors over their female peers by a slight margin. While such perceptions might not be representative of the actual teaching quality, it would be interesting in the future to delve deeper into the causes of possible biases.

Gas Permeation Properties of Soluble Aromatic Polyimides Based on 4-Fluoro-4,4'-Diaminotriphenylmethane.

A series of new organic polyimides were synthesized from 4-fluoro-4'4"-diaminotriphenylmethane and four different aromatic dianhydrides through a one-step, high-temperature, direct polycondensation in m-cresol at 180-200 °C, resulting in the formation of high-molecular-weight polyimides (inherent viscosities ~ 1.0-1.3 dL/g). All the resulting polyimides exhibited good thermal stability with initial decomposition temperatures above 434 °C, glass-transition temperatures between 285 and 316 °C, and good solubility in polar aprotic solvents. Wide-angle X-ray scattering data indicated that the polyimides were amorphous. Dense membranes were prepared by solution casting and solvent evaporation to evaluate their gas transport properties (permeability, diffusivity, and solubility coefficients) toward pure hydrogen, helium, oxygen, nitrogen, methane, and carbon dioxide gases. In general, the gas permeability was increased as both the fractional free volume and d-spacing were also increased. A good combination of permeability and selectivity was promoted efficiently by the bulky hexafluoroisopropylidene and 4-fluoro-phenyl groups introduced into the polyimides. The results indicate that the gas transport properties of these films depend on both the structure of the anhydride moiety, which controls the intrinsic intramolecular rigidity, and the 4-fluoro-phenyl pendant group, which disrupts the intermolecular packing.