Digital Explanation as Assessment in University Science

Assessments in tertiary science subjects typically assess content knowledge, and there is current need to both develop and assess different forms of knowledge and skills, such as communications and digital literacies. A digital explanation is a multimodal artefact created by students to explain science to a specified audience, which is an alternate form of assessment that has potential to develop and assess these other important forms of knowledge and skills. This research draws from perspectives in multimodality, educational semiotics and science education to gain a better understanding of digital explanation as a form of assessment in university science. Data sources include digital artefacts (n = 42), task descriptions and rubrics and pre-/post-interviews (n = 21) with students who created them as a task in a university science subject. Analysis involved identifying the range of media resources used across the data set, seeking patterns in how multiple resources were used and exploring students’ perspectives on the task, including their design decisions. A more detailed look at artefacts from three different science learning contexts illustrates that students base their design decisions on the content knowledge being represented, their technical capabilities to generate them and how to engage the audience. Students enjoy this form of assessment and feel that the tasks allowed them to demonstrate different sorts of capabilities than are normally assessed in their subjects. Recommendations for instructors provide guidance for considering this sort of task in tertiary science contexts.

Using the Learner-Generated Digital Media (LGDM) Framework in Tertiary Science Education: A Pilot Study

Learner-Generated Digital Media (LGDM) has become prevalent in higher education. Frameworks have been developed for video-making in the classroom that consider technical requirements, pedagogies, and the combination of both. However, missing is a practical model to guide academics and students on the implementation of LGDM assignments. This research aims to test a model to design, implement, and evaluate LGDM as an assessment tool. The model was built based on research gaps and it considers the following elements: (1) pedagogy, (2) student training, (3) hosting of videos, (4) marking schemes, (5) group contribution, (6) feedback, (7) reflection, and (8) evaluation. For this purpose, five science subjects (N = 270) were used to test the model as a guide to implementing LGDM assignments. Data was gathered using a validated 33-step questionnaire instrument. Additionally, group contributions were received using the SPARKPlus peer review application, and marks attained were gathered. Methodological triangulation of the datasets suggested that students have a positive attitude toward LGDM for science learning. Students enjoyed the group work and creativity, and they identified digital media support as a critical component of their learning experience. Preliminary data support using the LGDM framework to design digital media assignments for science education.

Development of a Web-based Intelligent Career Guidance System for Pre-Tertiary Science Students in Nigeria

The traditional approach to career guidance is a manual method that is ineffective and inefficient. The electronic approach provides an effective and efficient career guidance. This research therefore developed a web-based intelligent career guidance system that assists pre-tertiary science students in Nigeria to independently choose a career path at anytime and anywhere with the use of computer system or mobile/smart phones as applicants seek admission into various fields of study in Nigerian Higher Institutions (Universities or Polytechnics). The intelligent system uses student-driven parameters such as favourite science subjects combination, career interest inventory analysis result, and intelligent quotient test result for career recommendation. The web-based intelligent system was designed and implemented with principle of a rule-based expert system using forward chaining algorithm, the client-side/interface pages (front-end) were designed using “Bootstrap 3” front-end framework that contains HTML5, CSS3 and JavaScript. For the back-end, XAMPP was used. The system was implemented and evaluated using 200 pre-tertiary science students; they took the career choice tests and provided their feedback for the evaluation of the system performance. The feedback shows that the recommended career by the system is 95% accurate and relevant, 70% satisfactory, and 80% adequacy of information on career guidance by the system.

Comparative Evaluation of Online and In-Class Student Team Presentations

Student team presentations are commonly utilised in tertiary science courses to help students develop skills in communication, teamwork and literature research, but they are subject to constraints arising from class size, available time, and limited facilities. In an alternative approach, student teams present online using a variety of tools, such as screencast and blended media, but it is not clear whether this offers an authentic alternative to in-class experience. In this study, the two modes of presentation were compared in terms of student perceptions and academic performance. A survey probed students’ familiarity with digital technology, presentation anxiety, and differential perceptions of the two modes. Aside from a confirmation bias, no significant difference was found between those who presented in class and online. In a notable exception, a clear asymmetry appeared when students were asked to choose a mode for a future presentation: none of the online presenters opted for the in-class mode while a third of in-class presenters selected the online mode. Presentation anxiety was similar for in-class and online presenters and was insensitive to gender and familiarity with English. No significant difference was detected between the modes in terms of academic performance.