Beneficial effect of difficult learning tests: The effect was moderated by intelligence; no positive effect of testing for those of lower intelligence; average & especially higher intelligent learners got benefits

Relatively unintelligent individuals do not benefit from intentionally hindered learning: The role of desirable difficulties. Kristin Wenzel, Marc-André Reinhard. Intelligence, Volume 77, November–December 2019, 101405. https://doi.org/10.1016/j.intell.2019.101405

Highlights
• In two studies intelligence was positively correlated with later learning success.
• Study 2 also showed a beneficial effect of difficult learning tests.
• This effect was moderated by intelligence.
• There was no positive effect of testing for learners with lower intelligence.
• Average and especially higher intelligent learners profited from difficulties.

Abstract: Intelligence is an important predictor of long-term learning and academic achievement. In two studies we focused on the relation among intelligence, desirable difficulties–active generation/production of information and taking tests–, and long-term learning. We hypothesized that intelligence is positively correlated to long-term learning and that difficult learning situations, as opposed to easier reading, increase later long-term learning. We further assumed that the beneficial effects of difficult learning would be moderated by intelligence, thus, we supposed the positive effects to be stronger for learners with higher intelligence and weaker for learners with lower intelligence. We in turn conducted two experiments (N1 = 149, N2 = 176, respectively), measured participants' intelligence, applied desirable difficulties–generation/testing–in contrast to control tasks, and later assessed long-term learning indicated by delayed final test performance. Both studies showed positive correlations between intelligence and later long-term learning. Study 2 further found the expected beneficial effect of difficult learning, which was also moderated by intelligence. There was no difference between difficult tasks and control tasks for participants with relatively low intelligence. Retrieving answers in learning tests was, however, beneficial for participants with average intelligence and even more beneficial for participants with higher intelligence. In general, our two experiments highlight the importance of intelligence for complex and challenging learning tasks that are supposed to stimulate deeper encoding and more cognitive processing. Thus, specifically learners with higher, or at least average, intelligence should be confronted with difficulties to increase long-term learning and test performance.

Keywords: IntelligenceTesting effectRetrievalGeneration effectDesirable difficultiesLong-term learning

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4. Generaldiscussion
In two studies, we analyzed the linkage between participants' intelligenceandtheirlong-termlearning, as well as moderating effects of intelligence on difficult learning situations like generation and testing that are supposed to increase long-term learning. Asmentionedinthe introduction, intelligence has often been assumed to be one of the best predictors for learning and academic achievement, especiallyregarding complexand stimulatinglearning. Higher intelligence was further discussedtoincrease the effectiveness of intentionally hindered and more difficult learningsituationsandtobelinked to better and more effortful cognitive information processing. Theresults of our two studies highlight the importance of general intelligence and the inevitability of focusing on intelligence for predicting long-term learning. The positive linkage of intelligence and long-term learning remained robust and strong when controlling for participants' previous knowledge and when manipulating the learning situation. Moreover, although desirable difficulties, at least regarding tests in our second study, were also beneficial, intelligence even moderated the effectiveness of such difficult learning. This moderation effect regarding complex and difficult information is the most important contribution of our second study to the existing intelligence literature. Notably, tests were not more effective than re-reading control tasks for participants with relatively low intelligence but were beneficial for average and highly intelligent participants. Highly intelligent learners profited especially from using learning tests. Hence, intelligence was not only generallylinkedto long-term learning but also moderated situations, processes, andmethods thatwerespecificallyconstructed to increase long-term learning. This is in line with the above-mentionedtheoriesstatingtheimportanceofageneral intelligence factor for learning, success, and academic achievement in different contexts (e. g. , Kuncel et al., 2004; Roth et al., 2015; Spearman, 1904). Additionally, our results are similar to previous (controversial) research stating educational interventions and learning methods to be especially–or even only–advantageous for individuals with at least average cognitive abilities like intelligence: Thus, methods trying to improve longterm learning and academic achievement for everyone are often suggested to only further increase the disparity between high and low abilitylearners (see also the Matthew or rich-get-richer effects; e. g. , Rapport, Brines, Theisen, &Axelrod, 1997; Stern, 2015, 2017; Walberg&Tsai, 1983). Our results further support the literature assuming the importance of higher cognitive abilities for the beneficial effects of desirable difficulties (e. g. , Kaiser et al. , 2018; McDaniel et al. , 2002; Minear et al. , 2018). Our findings present a unique contribution to theunderstandingoftherole of intelligence forlearningingeneral, aswellasforstimulating learning situationsusing difficult, challenging, and complexmaterials. Thus, atleast average and higher intelligence facilitates effective deeper semantic encoding, cognitive processing, cognitive effort, and consolidation of information that is triggered by tests. Due to ourresults, we can advise the implementation of learningtests foruniversitystudents, at least for averagelyandhighly intelligentlearners. These profit from using difficult learning tests, even when applying a rather short, low-stake test only once. Fortunately, suchlearning tests areadvantageousfor a larger population of university studentsandcanbe implemented easily into university courses. Still, lecturers must remain vigilant that the applied learning testsareactually difficultandcomplex enough to trigger the beneficial effects. Concerning relatively unintelligent learners, we cannot unconditionally advise lecturers to use tests because suchlearners would have to indulge in difficult learning without profiting from it. Nonetheless, we also cannot advise against using difficult tests because at the very least, participants with lower intelligence suffered no disadvantages on their long-term learning due to the application of learningtests (see also the often assumed poor-get-poorer effect; e. g. , Stanovich, 1986). However, one might also argue that difficult learning is correlated withstressor frustration for less intelligent learners, because difficult tasks were in general found to increase perceived anxiety, and even low-stake quizzes were linked to pressure compared to a re-readingcontroltask (e. g. , Hinze&Rapp, 2014; O'Neil, Spielberger, & Hansen, 1969). Regarding generation tasks, implications are not that clea rbecause the manipulation of the learning condition in Study1was unsuccessful. In line with this, Study 1 did not result in a significant effect of the learning condition, thus, generation was not more beneficial than a reading control task. At the very least the generation tasks did not reduce participants'long-term learning, thus, they were not harmful. There were some positive and negative aspects of our studies that we care to mention and that could be applied or adapted in future work. For instance, the intelligence test we used was a rather detailed one with high quality factors; future research should use similar measures. This applies especially to the importance and predictivity of a general intelligencefactor. Still, we only used the basis module of the intelligence test, which measures a general intelligence factor similar to g or to fluid intelligence encompassing knowledge components. Future studies may add the existing knowledge tests to additionally assess fluid and crystalline intelligence so that more information regarding intelligence is available. Both of our studies used different curricular and realistic learning materials that are actually used in school and university courses; that said, the results can be generalized for actual learning materials and for information that is complex and difficult instead of relatively abstract learning of word pairs, vocabulary, or associations. It is vital that the difficult learning tasks are perceived as more difficult than the easier control tasks and that both conditions are clearly distinguishable. As a limitation, we only observed the influence of a single manipulated learning condition–one generation task or one learning test–on one single final test assessing long-termlearning. However, itis important to test if the moderating effects of intelligence remain the same when applying multiple learning tests or multiple re-reads over the course of an entire semester. In line with this, future studies should use multiple follow-up final tests to check if the effects change over time. Although the positive effect of intelligence was found in previous studies over long periods, the beneficial effect of tests could decline. One main limitation of our studies is that in regard to intelligence, we were only able to observe correlations. Although we did infer causal effects due to the different times of measurements of intelligence and long-term learning, further causal analyses are still advantageous. Future studies should implement longitudinal designs because these are supposed to serve as a basis for causal effects (cf. Strenze, 2007, 2015). All in all, there remain open questions regarding the tested linkage among intelligence, cognitive processes, generation, testing, and longterm learning. This applies for instance to the underlying effects of cognitive processing for learning. Although we argue that intelligence is positively correlated to better retrieval as well as to deeper processing of information, and although we know that higher intelligence is generallyimportantfor learning, wedonotknowexactlywhy. Thesame applies to the consideration of why desirabledifficultiesincrease cognitive processes that lead to higher long-term learning. It is possible that higherworkingmemory capacities, theability to handle simultaneously more pieces of information, the amount of cognitive resources, or higher memory skills are responsible for increased long-term learning. However, higher success could also be due to the abilities to reason, abstract thinking, or elaboration, or to higher processing speed, or simply to the ability to handle more cognitive effort and to overcome challenging tasks. So, in addition to general intelligence, futurestudies could focus on the linkage between even more aspects of cognitive abilities, likeprocessingspeed, workingmemorycapacity, memory, or reasoning, on long-term learning and the effectiveness of generation/ testing. Moreover, future work should also focus on increasing the benefit of desirable difficulties for learners with all–and especially lower–ability levels and not only for average or highly intelligent individuals. Thus, future studies may try to design difficulties that are adequately difficult for every individual; the tasks should be difficult enough to elicit the beneficial effects of desirable difficulties but still easy enough that learners with lower intelligence are able to overcome them without being completely overwhelmed (see e. g. , Minear et al. , 2018). Future studies should there foremonitor and test which level of difficulty is beneficial for which individual. Lecturers could, for instance, also give lower ability learners more time or apply graded learning aids to support them (see e. g. , Hänze, Schmidt-Weigand, & Stäudel, 2010). Besides, researchers could test if lowerability learners would benefit from longer initial learning phases or from applications of desirable difficulties later in the learning process when these learners have already mastered some of the basic information or formed sufficient previous knowledge (see also the above-mentioned expertise-reversal effect or the aptitude-treatment-interaction; e. g. , Kalyugaetal. , 2003; Snow, 1989). Future work could also test if multiple applications of desirable difficulties or the usage of tests in high-stake learning situations in actual university courses may improve long-term learning forlowerabilityindividuals. In general, future work could also use a more naturalsetting, awithinsubjectdesign, or it could even implement further difficulty nuancesregarding the information as well as the desirable difficulties themselves. Although the forced application of learning tasks is rather common in university courses, it is advantageous to explore the effects of intelligence and desirable difficulties using self-regulated learning. Thus, one could explore if intelligence also moderates the decision to use generation tasks or tests instead of relatively easy re-reading tasks, and also if intelligence moderates learners' persistence while working on such difficulties.