Some methods designed to “improve the student” offer a general formula that can be applied to a wide variety of content. A famous and longstanding example is the SQ3R method, which can be applied to many types of content, but is particularly suited for improving reading comprehension. The SQ3R method, still recommended by some universities, was based on psychological and educational research and was propelled by the need to rapidly train personnel for World War II (Robinson, 1978; originally 1946). The SQ3R name is shorthand for five recommended steps that are to replace simply reading: survey, question, read, recite, and review.

An example of a more targeted way to “improve the student” is by teaching specific skills that are useful for learning particular content (e.g., Palincsar & Brown, 1984; Pressley & Woloshyn, 1995; Segal, Chipman, & Glaser, 1985). The assumption behind specific-skill training is that a student can apply specific learning and metacognitive strategies to facilitate learning (e.g., Palincsar & Brown, 1984). For specific-skill training to be effective, teachers must teach the details of strategies and show students how the strategies apply to a variety of situations (McCormick & Pressley, 1997). To be successful, students need to understand why, how, and when particular strategies will work (Pressley & Woloshyn, 1995).

In addition to general how-to-study methods and specific-skill training, there are many important expositions, based on decades of cognitive and educational psychology research, about how people learn and about what students know or think they know about knowledge and skill acquisition. Some of these emphasize particular study skills that students can learn and apply or that teachers can build into their teaching routines (e.g., Pashler et al., 2007). Other publications provide such advice and also place considerable emphasis on metacognitive accuracy and on the metacognitive misconceptions people have about how they learn, and about their judgments of how well something has been learned (e.g., Bjork, Dunlosky, & Kornell, 2013; Brown, Roediger, & McDaniel, 2014).

A major challenge of methods for “improving the student” is that the student (or more generally, the learner) must first learn a variety of effective study techniques; then must be able to choose a technique to match a particular learning environment, content, and desired outcome; and also must be willing to invest the effort required to employ the appropriate techniques.

A prerequisite for “improving the student” is to determine, through research, which techniques are of most value for learning. Several commonly used study techniques have been reviewed in detail and have been scored for overall utility (or value) by considering whether they are suitable for use across a variety of learning conditions, student characteristics, materials, and tasks (Dunlosky, Rawson, Marsh, Nathan, & Willingham, 2013). Of the 10 techniques evaluated, practice testing and distributed practice were found to have the highest utility. Elaborative interrogation, self-explanation, and interleaved practice were rated to be of moderate utility. For a variety of reasons, summarization, highlighting, the keyword mnemonic, imagery use for text learning, and rereading were rated as having low utility.

In these ratings, utility primarily reflects the design dimension of usefulness, but the ratings for utility also include considerations of a study technique's usability and aesthetics. If a technique facilitates learning for multiple learning conditions, student characteristics, materials, and tasks, then its usefulness and therefore, its utility, would be rated higher. However, if a technique is difficult to implement for some learning conditions, student characteristics, materials, or tasks, then its usability and therefore, its utility, would be rated lower. If the technique is sometimes unpleasant (e.g., tedious or boring), its aesthetic success is poor for those situations, and so the technique's utility will be lower. Although the techniques that are rated highest in utility can be recommended for most situations, techniques rated lower in utility may still be effective for a subset of learning conditions, student characteristics, materials, and tasks. Consequently, a student needs to learn to identify the most appropriate techniques (as defined by the design dimensions of usefulness, usability, and aesthetics) to match the particular learning conditions, the student's personal characteristics, the to-be-learned materials, and the tasks. All of this requires the student to have a great deal of metacognitive awareness, as well as to have experience learning related content and doing similar tasks.

A large part of the student's challenge is to resist doing things that seem productive but that are actually not optimal for long-term retention. For example, the student must choose to self-test rather than to simply reread the to-be-learned content and also must choose to space practice over several days rather than to mass practice immediately before a test. Such choices require three things: having knowledge of what works best for learning, planning a study schedule, and applying self-discipline to follow the plan under typical time constraints.

Another method of improving the to-be-learned content is to provide various sorts of previews and organizers that students can use to understand new material. For example, to facilitate comprehension of new material in a reading assignment, related but more general content can be provided as an “advance organizer” for the new content (Ausubel, 1960, 1968; Ausubel, & Fitzgerald, 1962). Within this vein, it has been shown that providing outlines and diagrams can improve learning from lectures (Bui & McDaniel, 2015).

Another important way to improve content design, especially in domains such as mathematics that emphasize problem solving, is to include several completed examples that show the exact problem-solving steps (Kalyuga, Chandler, & Sweller, 2001; Renkl & Atkinson, 2003; Sweller & Cooper, 1985).

When feasible, combining simultaneous complementary verbal and visual information to create multimedia content can facilitate learning (e.g., Mayer, 2004, 2014). If, however, there is redundancy in the visual and verbal information (for example, presenting a text summary of the auditory content), multimedia presentation can impede learning because of information-processing competition (Mayer, Heiser, & Lonn, 2001), so it is important that the contents of different modalities complement, rather than compete with, one another.

The positive learning effects of organizing and clarifying content have been explained by cognitive load theory which says, for example, that providing structure reduces cognitive demands imposed on students (Paas, Renkl, & Sweller, 2003; Sweller, 2016; Sweller & Chandler, 1994). In contrast, some examples of content design suggest that increasing cognitive demands can result in better learning. For example, McNamara, Kintsch, Songer, and Kintsch (1996) reported that text comprehension by students who had a strong background in the presented subject matter benefited in some ways from a less coherent text. The theoretical explanation offered is that students engage in compensatory processing to infer unstated relations in the text and that this additional processing results in better performance on subsequent measures of comprehension.

A related result, showing better retention after increased cognitive demands, comes from studies of learning-from-tests. In one experiment, initial recall of word pairs decreased as the initial test delay increased, but on a later second test, recall of the word pairs increased as the initial test delay increased (Whitten & Bjork, 1977). One theoretical explanation is that learners, in some sense, must work harder or process information differently to perform the more delayed initial retrieval and that this effort results in better long-term recall. Situations with such counterintuitive results, where increasing cognitive effort decreases immediate performance but facilitates long-term learning, have been described as “desirable difficulties” (Bjork, 1994). Other examples of such “desirable difficulties” that can be designed into the to-be-learned content include spacing instruction on a specific topic rather than massing instruction on that topic, interleaving topics rather than focusing completely on one topic at a time, and integrating multiple practice tests into the to-be-learned content (Bjork & Bjork, 2014).