In recent years, the Saudi Arabian government and educationists have expressed concerns about the low level of achievement in English among students in schools and universities. To improve English learning and achievement in Saudi Arabia, many research studies in motivation and learning have shown that four major indicators of active learning are: (i) group work, (ii) situated learning, (iii) elaborated feedback, and (iv) information communication technology [ICT] use in classroom instruction. This explanatory mixed-methods study, longitudinal in nature, explored the use of these four indicators of active learning on the enhancement of Saudi students' motivational constructs (i.e., self-efficacy, task value, academic buoyancy, and effort expenditure), engagement (i.e., vigor, dedication, and absorption), and English achievement. In particular, the present study sought to investigate two main research aims: (i) the impact of an eight-week intervention program (incorporating the four indicators of active learning) on students' motivational constructs, engagement, and academic achievement in English, and (ii) the relationships between the motivational factors, engagement, and academic achievement at three time points (e.g., the predictive impact of Time 1 self-efficacy on Time 1 academic buoyancy). Participants of this study were 289 male university students enrolled in an English unit at the University of Hail in Saudi Arabia. The quantitative phase of this study encompassed experimental and correlational emphasis, involving the undertaking of a two-group experimental comparison with 145 participants in the Experimental Group and 144 participants in the Control Group. Data collection during the quantitative phase spanned three time points: Time 1 (collected before the intervention), Time 2 (collected in the middle of the intervention), and Time 3 (collected after the intervention). At each of the three time points, the students of both the Experimental Group and the Control Group completed the same measure of the motivational variables, engagement, and English achievement test. Upon the completion of Time 3, a qualitative component (in the form of semi-structured interviews) was conducted with students from the Experimental Group to obtain deeper insights into the effectiveness of the intervention program. The findings of the first aim of this study indicated that the Experimental Group, exposed to the intervention program including the four indicators of active learning, scored significantly higher than the Control Group on the motivational constructs, engagement, and English achievement. Specifically, the results of repeated measures ANOVA and follow-up t-tests showed that the intervention had small effects on all the variables at Time 2 (in the middle of the intervention). However, at Time 3 (after the intervention), the intervention had small impacts on task value and effort expenditure, moderate impacts on dedication and academic achievement, and large impacts on self-efficacy, academic buoyancy, vigor, and absorption. The qualitative semi-structured interviews augmented these findings by providing a vital context for in-depth understanding of how and what aspects of each of the four indicators of active learning contributed to the gains in the motivational variables, engagement, and English achievement. To address the second aim of this study (i.e., the relationships between the motivational variables, engagement, and academic achievement at Time 1, Time 2, and Time 3), structural equation modelling procedures were used. The results yielded some key findings, supporting in part the hypotheses tested. For example, Time 1 self-efficacy significantly predicted Time 1 academic buoyancy; Time 1 task value significantly predicted Time 1 effort expenditure; and Time 1 vigor significantly predicted Time 1 academic achievement. In general, the evidence obtained provides important implications for further research development and educational practices.

Learning mathematics and science entails learning the relations among multiple interacting elements, especially when solving problems. Assimilating multiple interacting elements simultaneously in the limited working memory capacity would incur cognitive load. Unless the instructions provide a mechanism to manage the high cognitive load involved, learning effectiveness may be compromised. Researchers have investigated instructional efficiency across diverse domains from the perspective of cognitive load theory. Progress in educational theory has enabled a better understanding of three types of cognitive load that students experience during the learning process: intrinsic, extraneous, and germane cognitive load. Processing the intrinsic nature of a task constitutes intrinsic cognitive load (e.g., complexity of elements). Sub-optimal instruction requiring unnecessary processing of elements constitutes extraneous cognitive load. Investing mental effort in multiple practices constitutes germane cognitive load. Recent advance in cognitive load theory highlights element interactivity (i.e., the interaction among elements to be processed) as a common thread among different types of cognitive load. However, despite progress in cognitive load research, little is known about the effects of element interactivity in secondary school mathematics and science education. Using element interactivity as a point of reference, this article reviews the design features of different approaches to teaching linear equations in mathematics and the topic of density in science. Evidence seems to point to the practical benefit of using instructional approaches that address the issue of multiple elements interacting with each other to facilitate learning. As such, the conceptualization of cognitive load in terms of element interactivity will bring further progress in the research on cognitive load in mathematics and science learning.