Overall, the effects of deictic gestures on learning and memory were mixed. The studies in Part 1 showed that observing a video model’s deictic gestures had only a small positive effect on time on task for the older adults, but not for the children and the young adults. The studies in Part 2 showed that producing deictic gestures had a positive effect on visuospatial source- and working memory in young and older adults, but only if gesturing or not gesturing (naming or observation only) was manipulated within subjects and not between subjects.
Mixed results of studies investigating effects of deictic gestures on learning are not uncommon; some studies show a positive effect of deictic gestures on learning (e.g., De Koning & Tabbers, 2013; Hu, Ginns, & Bobis, 2015; Macken & Ginns, 2014; Valenzeno, Alibali, & Klatzky, 2003) while others found a negative effect (e.g., Post, Van Gog, Paas, & Zwaan, 2013, for children with lower levels of language ability), no effect (Craig, Gholson, & Driscoll, 2002; Post et al., 2013, for children with higher levels of language ability) or mixed results (e.g., Baylor & Kim, 2009). Note that some of these studies investigated children’s learning (Hu et al., 2015; Post et al., 2013; Valenzeno et al., 2003), while others investigated young adults’ learning (Baylor & Kim, 2009; Craig et al., 2002; De Koning & Tabbers, 2013; Macken & Ginns, 2014). However, to the best of my knowledge, no research has been conducted yet on the effects of deictic gestures on older adults’ memory and learning in itself or in comparison with other age groups.
Because the studies in Part 1 did not show the expected positive effects of gestures on learning outcomes, it can only be concluded that observing deictic gestures neither hampered nor improved learning compared with no gestures or symbolic cues for the particular problem- solving task used. Although no positive effects of gestures on learning from a video-modeling example were found, it must be noted that the specific task used in the studies reported in Chapters 2 and 3 might perhaps not have been sensitive to the instruction conditions used.
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An alternative explanation for the null result can be found in the design of the studies. For example, in the study of Hu et al. (2015) and Macken and Ginns (2014) no video-based modeling examples or any other form of animations were used, but participants received written instructions to point and trace themselves in worked examples. The studies of De Koning and Tabbers (2013) and Valenzeno et al. (2003) were more comparable with the present studies because they used an animation with pointing and tracing gestures as cues (De Koning & Tabbers, 2013) or a video instruction with a human model using pointing and tracing gestures (Valenzeno et al., 2003). However, these studies used one instruction that was only shown once to the participants, while in the studies presented in Part 1 of this dissertation multiple videos were used. In the study presented in Chapter 2, participants received a 180 s instruction about the general task rules followed by four video-based worked examples that partly overlapped in problem structure. In the study presented in Chapter 3, participants received one video-based modeling example, twice. It is possible that the five videos shown in the study presented in Chapter 2 covered the information to such an extent that the gestures did not add to the learning anymore and that the repetition of the worked example in the study presented in Chapter 3 caused any learning effect after watching the video the first time to disappear. In addition, the videos all had durations between 90 s and 180 s (180 s for the general instruction, 90 s for the two-step problems and 120 s for the three step problems). If the effect of gesturing is short-lived, the length of the videos might also have induced a decay of any possible effects during the test.
The effects gestures can have on memory and learning may vary depending on types of tasks, gestures and learners. Not only do task demands vary (e.g., procedural vs. declarative memory/learning), there are also all kinds of gestures (e.g., deictic gestures, such as pointing, representational gestures, beat gestures, emblems), manners of gesture use (observed or self- performed), and learner characteristics (e.g., age, proficiency in certain areas, intelligence, experience) that might play a role in the effectiveness of gesturing for learning. Connecting the right type of gesture to the right task for the right type of learners is a complex puzzle. This dissertation tried to find some (small) pieces of that puzzle by studying the effects of deictic gestures on different types of memory and learning tasks in different age groups.
Despite the fact that deictic gestures did not have a positive effect on learning in the studies described in Part 1, pointing did seem to help guide learners’ visual attention toward task relevant areas on crucial time points during the instruction. This makes deictic gestures a promising cueing tool in dynamic visualizations (such as video or animations) that show information in a transient manner, meaning that it should be attended to timely or is no longer available for processing.
In Part 2, the contradictive findings between the studies reported in Chapters 4 and 5 concerning the effect of pointing toward pictures on spatial source memory, were explained by a ‘selectivity’ account; if the task requires a selection process for a subset of the stimuli,
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pointing facilitates further processing and memory for these pointed items compared with the items that are only observed. In daily life, selectivity is part of almost all our behavior; every action is selected from a variety of other possible actions that need to be inhibited. Therefore, the results of the study reported in Chapter 4 might be more informative with regard to practical implications regarding the use of pointing gestures than those in Chapter 5,
The study described in Chapter 6 showed that a multimodal compared with a unimodal encoding strategy improved visuospatial working memory performance in both young and older adults (Experiment 1) and that adding visual cues to the multimodal but not the unimodal encoding strategy improved the performance of older adults on the visuospatial working memory task up to the level of young adults. Furthermore, a multimodal encoding strategy together with predictive visual cues can ameliorate temporal decay in working memory in both young and older adults. These findings are especially interesting from an aging perspective, because it suggests that gestures and visual cues can be used as tools to compensate for age-related declines in visuospatial working memory performance (at least, in the present paradigm).
An implication of the results presented in Part 2 is that self-produced gestures can enhance spatial source memory and visuospatial working memory. It is possible that via the attentional bias toward objects we tend to act upon and the fact that pointing forces our attention toward a certain location, this type of body-based involvement in memory encoding can be used in learning different kinds of tasks that target visuospatial skills.