This article compared online instruction and face-to-face instruction at University Malaysia Sarawak (UNIMAS). Participants were undergraduate full-time students who were enrolled in the Human Resource Development Program. The learning materials used was 'How to write research proposals and reports'; and this represented a subtopic in the 'Research Methods' course. The design of an online learning environment emphasized four types of interaction: learner-content interaction (topic notes), learner-self interaction (multiple exercises), learner-learner interaction and instructor-learner interaction (online discussion on case studies and group project). The 'QuickPlace' software was customized to incorporate the component of multiple choice exercises. This latter was written with html language and linked to the 'QuickPlace'. The face-to-face group attended routine lectures and tutorials on the same topic. Test results indicate that the online discussion assisted students to learn case studies slightly better than the face-to-face instruction. This may due to the above learning interactions that resulted in a greater emphasis on self-oriented and group-oriented learning as compared to an instructor-oriented face-to-face learning experience. However, feedback from the students indicate a need to further improve the design of the online course.

The forms of feedback employed in online education are a significant aspect of the instructional design of online subject offerings. At Tabor Adelaide, a private multi-denominational Higher Education and VET education provider, the feedback aspects of this year's online subject offerings were investigated. In this research project the feedback embedded in online subjects was examined on the basis of principled, empirically-based research findings. Based on the best principles of educational feedback derived in a major recent review of formative educational feedback research (Shute, 2008) a set of feedback questions pertinent to online education were generated and used as a basis for examining the feedback embedded in all the semester one online subject offerings at Tabor Adelaide.

The degree of element interactivity determines the complexity and therefore the intrinsic cognitive load of linear equations. The unpacking of linear equations at the level of operational and relational lines allows the classification of linear equations in a hierarchical level of complexity. Mapping similar operational and relational lines across linear equations revealed that multi-step equations are derivatives of two-step equations, which in turn, are derivatives of one-step equations. Hence, teaching and learning of linear equations can occur in a sequential manner so that learning new knowledge (e.g. two-step equations) is built on learners' prior knowledge (e.g. one-step equations), thus reducing working memory load. The number and nature of the operational line as well as the number of relational lines also affects the efficiency of instructional method for linear equations. Apart from the degree of element interactivity, the presence of complex element (e.g. fraction, negative pronumeral) also increases the complexity of linear equations and thus poses a challenge to the learners.

We compared the equation approach and unitary approach in helping students (n = 59) learn percentage change problems from a cognitive load perspective. The equation approach emphasized a two-part learning process. Part 1 revised prior knowledge of percentage quantity; Part 2 integrated the percentage quantity and the original amount in an equation for solution. Central to the unitary approach is the concept of unit percentage (1%). The unitary approach would expect to incur high element interactivity because of the intrinsic nature of its solution steps, and the need to search and integrate quantity and percentage in order to act as a point of reference for calculating the unit percentage. Test results and the instructional efficiency measure favored the equation approach. It was suggested that the equation approach reduced the intrinsic cognitive load associated with percentage change problems via sequencing and prior knowledge.

Mobile-learning (M-Learning), also known as mobile assisted learning, has emerged over the years as a non-traditional format of teaching and learning. This pedagogical approach, facilitated with the advent of technological advances, may include the use of portable devices, such as MP3 players, tablets, e-books, cell phones, and smartphones. There is extensive research that has been undertaken, providing empirical yields for further research consideration. In this chapter, a special focus on mobile-learning, we explore the importance of this pedagogical approach from the perspective of motivation. We argue that research, to date, has yet to examine the situational placement of mobile-learning within the sociocultural context of motivation. In our quest to promote and develop the notion of mobile-learning, it is important that we take into account psychosocial issues that could explain its successes and failures. Is mobile-learning simply a transient fad that will fade away with the passing of time? Why would we engage in mobile-learning whenthere are so many defining limitations, such as small screen size, one-finger typing, etc.? Our conceptualization, developed in motivational contexts, seeks to identify and discuss four notable issues: (i) the importance of cognitive load theory, (ii) a constructivist paradigm for learning, (iii) the introduction of effective functioning as a personal well-being component, and (iv) the social world and its ongoing disparities, leading to imbalances between individuals. Our theoretical examination, balanced in its positioning, makes attempts to situate the concept of mobile-learning within the framework of motivation.

We examined the use of balance and inverse methods in equation solving. The main difference between the balance and inverse methods lies in the operational line (e.g. +2 on both sides vs -2 becomes +2). Differential element interactivity favours the inverse method because the interaction between elements occurs on both sides of the equation for the balance method but only on one side of the equation for the inverse method. In an experimental study, 63 students (mean age = 13) were randomly allocated to either balance or inverse group to undertake a pre-test, study an instruction sheet, complete acquisition equations, sit for a post-test and a concept test. Procedural knowledge was assessed on performance on practice equations and post-test, whereas conceptual knowledge was assessed on performance on the concept test. The inverse group outperformed the balance group on practice equations but not the post-test. Both the balance and inverse groups scored higher on the inverse concept test than the balance concept test. Positive association between performance on procedural knowledge and performance on conceptual knowledge was found for the inverse group but not the balance group. Overall, the evidence obtained indicates a number of educational implications for implementation.

Assimilating multiple interactive elements simultaneously in working memory to allow understanding to occur, while solving an equation, would impose a high cognitive load. Element interactivity arises from the interaction between elements within and across operational and relational lines. Moreover, operating with special features (e.g. negative pronumeral) poses additional challenge to master equation solving skills. In an experiment, 41 8th grade students (girls = 16, boys = 25) sat for a pre-test, attended a session about equation solving, completed an acquisition phase which constituted the main intervention and were tested again in a post-test. The results showed that at post-test, students performed better on one-step equations tapping low rather than high element interactivity knowledge. In addition, students performed better on those one-step equations that contained no special features. Thus, both the degree of element interactivity and the operation with special features affect the challenge posed to 8th grade students on learning how to solve one-step equations.

From the perspective of cognitive load theory, the complexity of equation solving depends on the degree of element interactivity, which is proportionate to the number of operational and relational lines. An operational line alters the problem state of the equation, and yet at the same time preserves its equality (e.g., + 2 on both sides). A relational line indicates the relation between elements in that the left side of the equation equals to the right side. Apart from the element interactivity effect, operating with special features (e.g., fractions) increases the complexity involved in equation solving. Thirty-eight pre-service teachers (Female = 30, male = 8) were randomly assigned to solve one-step, two-step or multi-step equations and to complete a concept test regarding the role of '=' sign with respect to the operational and relational lines. Test results revealed that higher performance correlated with fewer number of operating and relational lines. However, performance favored those equations without special features when the number of operational and relational lines was kept constant. The correlation between performance on test items and concept test was significant for both two-step equations and multi-step equations but not for one-step equations.