Impact of digital technologies upon teaching and learning in higher education in Latin America: an outlook on the reach, barriers, and bottlenecks

Digital technology and literacy can heighten the transformation of teaching and learning in higher education institutions (HEIs). This study uncovers the extent to which digital technologies have been used to advance the teaching and learning process in HEIs, and the barriers and bottlenecks to why it may not have been effectively implemented across the HEIs. The study used nine selected countries in Latin America (LATAM) based on the main focus of the educators, commercial, and financial investors; to show the level of impact/implications of computer technologies on the teaching and learning processes. We applied a two-step (mixed) methodology (through a quantitative and qualitative lens) for the research investigation, using data collected from survey we administered to faculty members in HEIs across the different countries in LATAM. In turn, we implemented a Text Mining technique (sentiment and emotional valence analysis) to analyze opinions (textual data) given by the participants to help determine challenges and obstacles to using the digital technologies for teaching and learning in the region. Quantitatively, we applied a Kruskal-Wallis H-test to analyze the collected multiple choice and ranked items in the questionnaire in order to identify prominent factors that consummately influence the reach, barriers, and bottlenecks, and where the differences may lie across the different LATAM countries. The results show that the users upheld the emphasis on lack of training, infrastructures and resources, access to internet and digital platforms, as the main challenges to the teaching-learning process. The study also empirically discussed and shed light on critical factors the HEIs, particularly in LATAM, should resolve and adopt in support of the decision-making strategies, operational policies and governance, financial investments, and policymaking, at a time when "digital technologies" have become an inevitable and indispensable part of education and learning.

Impact and future of telemedicine amidst the COVID-19 pandemic: a systematic review of the state-of-the-art in Latin America

Significant progress has been made in using information and communication technologies in medicine, by impacting the quality of health-care delivery system and patient care, and paving the way for ground-breaking tools for e-health and clinical decision-support systems. This study investigates the extent to which the evolution of telemedicine applications has been used to support patient care in Latin America (LATAM) amidst the pandemic. Theoretically, the study applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to identify the impact of telemedicine in the region. Practically, the paper provides a systematic mapping study of the different domain areas and methodological progress in Telemedicine that occurred during the pandemic, and applied a text mining technique to understand the intensities of the terms expressed by the analyzed studies. The results show that while telemedicine has not been extensively used, a greater percentage of the studies report that telemedicine was effective. Approximately 70% positive emotional valence score was found. The paper also provides an empirical discussion and recommendations for the next steps in ample adoption of telemedicine.

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.

Gamifying Sexual Education for Adolescents in a Low-Tech Setting: Quasi-Experimental Design Study

BACKGROUND

Sexual education has become increasingly important as unhealthy sexual practices and subsequent health risks become more prevalent during adolescence. Traditional sex education teaching methodologies are limiting for digital natives exposed to various digital technologies. Harnessing the power of technology applications attractive to the younger generation may be a useful approach for teaching sex education.

OBJECTIVE

The aim of this study was to improve sexual health knowledge and understanding of the problems associated with unhealthy sexual practices and address sexual and reproductive health challenges experienced in a low-tech setting.

METHODS

A participatory design approach was used to develop the digital gamified methodology. A sample of 120 secondary school students aged 11-15 were randomly assigned to either experimental or control group for each of the 3 teaching approaches: (1) gamified instruction (actual serious games [SG] in teaching); (2) gamification (GM; making nongames, such as game-like learning); and (3) traditional teaching (TT) methods.

RESULTS

The SG and GM approaches were more effective than TT methods in teaching sexual health education. Specifically, the average scores across groups demonstrated an increase of mean scores from the pre- to posttest (25.10 [SD 5.50] versus 75.86 [SD 13.16]; t₁₁₉=41.252; P<.001 [2 tailed]). Analysis of variance indicated no significant differences across groups for pretest scores (F_2,117=1.048, P=.35). Significant differences across groups were evident in the posttest scores. Students in the SG and GM groups had higher average scores than the TT group (F_2,117=83.98; P<.001). Students reported increased learning motivation, attitude, know-how, and participation in learning (P<.001) when using SG and GM approaches.

CONCLUSIONS

Digital health technologies (particularly teaching and learning through gamified instruction and other novel approaches) may improve sexual health education. These findings may also be applied by practitioners in health care settings and by researchers wishing to further the development of sex education.

Technology-mediated teaching and learning process: A conceptual study of educators' response amidst the Covid-19 pandemic

The COVID-19 pandemic has disrupted many areas of the human and organizational ventures worldwide. This includes new innovative technologies and strategies being developed by educators to foster the rapid learning-recovery and reinstatement of the stakeholders (e.g., teachers and students). Indeed, the main challenge for educators has been on what appropriate steps should be taken to prevent learning loss for the students; ranging from how to provide efficient learning tools/curriculum that ensures continuity of learning, to provision of methods that incorporate coping mechanisms and acceleration of education in general. For several higher educational institutions (HEIs), technology-mediated education has become an integral part of the modern teaching/learning instruction amidst the Covid-19 pandemic, when digital technologies have consequently become an inevitable and indispensable part of learning. To this effect, this study defines a hybrid educational model (HyFlex + Tec) used to enable virtual and in-person education in the HEIs. Practically, the study utilized data usage report from Massive Open Online Courses (MOOCs) and Emotions and Experience Survey questionnaire in a higher education setting for its experiments. To this end, we applied an Exponential Linear trend model and Forecasting method to determine overall progress and statistics for the learners during the Covid-19 pandemic, and subsequently performed a Text Mining and Univariate Analysis of Variance (ANOVA) to determine effects and significant differences that the teaching-learning experiences for the teachers and students have on their energy (learning motivation) levels. From the results, we note that the hybrid learning model supports continuity of education/learning for teachers and students during the Covid-19 pandemic. The study also discusses its innovative importance for future monitoring (tracking) of learning experiences and emotional well-being for the stakeholders in leu (aftermath) of the 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.