Several studies have focused on identifying the significant behavioral predictors of learning performances in web-based courses by examining the log data variables of learning management systems, including time spent on lectures, the number of assignments submitted, and so forth. However, such studies fail to quantify the impact of emotional, motivational, behavioral, and cognitive–metacognitive factors simultaneously. This research was an attempt to understand the relations between students’ motivation, cognitive–metacognitive strategies, behavior, and learning performance in the context of blended courses in higher education. Then, relevant predictors are used to obtain a model to classify the students’ performance and to identify those who are at risk of failing the course. The authors conducted an empirical study in a higher educational course with 137 Mexican students. Nineteen variables related to emotions, motivation, cognitive–metacognitive strategies, and behavior. Only six were found to be significant. These variables explain approximately 67% of the variance between each student’s overall grade. The model, based on those variables, correctly classifies 96% of the students.

Due to a variety of barriers to develop innovation, small and medium enterprises (SMEs) find it necessary to collaborate with external sources of knowledge. The current study analyses the collaboration between SMEs and academia over an open innovation setting in Mexico. An absorptive capacity (ACAP) approach has been applied to understanding the process of developing new knowledge for achieving innovation. A two-part questionnaire was developed with the aim of assessing the ACAP of a new joint research unit. Data was collected from a local group of SMEs that collaborated as dyads with academia supported by a government program of innovation in Mexico. The result shows that there was a moderate potential and realized ACAP in the sample; these results are mutually related with both parts of the questionnaire which supports our findings. In conclusion, exploitation of new knowledge is a complex dimension for creating value from collaboration, which makes the outcome difficult to measure using traditional means. It can be argued that exploiting new knowledge for innovation is an iterative process of learning when exploring new sources of knowledge from academia.

During the last decades, digital image processing algorithms have been developed to measure external characteristics of agricultural products due to the great potential that these methods offer. So, in this research, the thermal images obtained from a thermographic camera were analysed considering two genotypes of maize seeds: crystalline and floury in their natural state, previously irradiated with a laser light source of 650 nm for exposure times of 15 s and 35 s. The methods applied in the analysis were: a) histogram to obtain the distribution of gray levels of images, b) mean value that indicates the brightness of images, c) variance which means the contrast of images, d) entropy applying both Shannon and Tsallis definitions, which provide the average self-information of images, e) estimation of the probability density of temperature variations on seeds to quantitatively characterize them from thermal images. Higher mean and variance were obtained from crystalline seeds indicating higher brightness and contrast. Furthermore, thermal images of floury seeds had higher entropy of Shannon indicating that images had greater disorder with respect to images of crystalline seeds. In the case of the entropy of Tsallis, the entropic index q could be used for characterization of seeds. Thermal images obtained from seeds with a floury structure provided a higher redundancy value for a shorter exposure time to laser light. Thus, the viability of the statistical methods of digital image processing applied to thermal imaging for the characterization of seeds is shown.