Juan Carlos Vega Garzón

Using augmented reality to teach and learn biochemistry

Understanding metabolism and metabolic pathways constitutes one of the central aims for students of biological sciences. Learning metabolic pathways should be focused on the understanding of general concepts and core principles. New technologies such Augmented Reality (AR) have shown potential to improve assimilation of biochemistry abstract concepts because students can manipulate 3D molecules in real time. Here we describe an application named Augmented Reality Metabolic Pathways (ARMET), which allowed students to visualize the 3D molecular structure of substrates and products, thus perceiving changes in each molecule. The structural modification of molecules shows students the flow and exchange of compounds and energy through metabolism.

El potencial de las aplicaciones educativas en el proceso de evaluación formativa

The debate about real objectives of assessment is not new; however, currently still predominating some aspects, such as the enhancement of memory and practice that reflects a traditionalist and limited approach. It is necessary to overcome the exclusive use of traditional assessments in order to adopt a formative assessment. This modality seeks to identify student gaps and needs in the educational process, to receive feedback and take necessary decisions to improve teaching and learning processes. To meet this imminent need and seeking to assist teachers in implementing a formative evaluation, some technological resources have been developed. This study aimed to describe the potential of three applications (Armet, The Cell, and 3DClass) in the formative evaluation process, through the databases associated with them. Results show that these applications provide more transparent and consistent evaluation metrics, enabling the teacher to systematize criteria and indicators, reducing the subjectivity of the formative assessment process and the time spent for processing, tabulation, and data analysis. Considering that it is possible to check what issues were recurring errors, total quantity of mistakes, what questions had more level of success and how long they took to develop the activity. They also allow ascertaining whether there has been growth over time or even identifying where errors occur; allowing a thorough investigation on the possible causes, student’s feedback, as well as pointed and directed teacher interventions.

QUESTIONS ABOUT QUESTIONS: EVALUATING THE QUALITY OF ASSESSMENTS IN THE DATA SCIENCE ERA

INTRODUCTION: Assessments are not tools for merely grading students. They can be powerful instruments to monitor, measure, and improve both students learning and teaching process. In the Data Science era, educational technology has an important role collecting, storing and organizing data, allowing instructors to assess a large amount of students even remotely. Thus, once assessment is of central importance to education it is desired that these instruments are reliable and valid. OBJECTIVES: This work aims to describe the potential of validation tools united to educational technology and Rasch Analysis to improve assessments quality. MATERIALS AND METHODS: Three different virtual environments were used to collect data to this study: 3DClass, The Cell and ARMET, mobile applications able to track students’ performance answering quizzes. First, learning objectives were defined for each software purpose. Then, questions were created to assess students learning throughout quizzes. These questions were peerreviewed by experts and then applied to students. After that, the data were refined and analyzed using Rasch Model. DISCUSSION AND RESULTS: Results show that software used in this study provided detailed and organized data regarding students’ behavior, in large scale. From this data, the Rasch model identified overall reliability of the quizzes applied, pointing questions in need of reformulation, consequently identifying students’ abilities that were not estimated precisely. Moreover, we analyzed the assessments level of difficulty in relation to students sample in order to develop new questions that match them. From this data, instructors were able to reformulate the assessments to match course objectives and provide feedback to students in real time. CONCLUSION: This methodology allowed us to refine quizzes in every application to students, establishing criteria to choose which questions we would keep or remove from quizzes, improving their quality in terms of learning objectives and questions reliability.