Investigating Distribution of Practice Effects for the Learning of Foreign Language Verb Morphology in the Young Learner Classroom

Skill Acquisition and Practice Distribution

Practice plays a critical role in skill acquisition theories of learning (DeKeyser, 2015). Deliberate, intensive practice enables learners to move from an initial reliance on declarative, explicit knowledge to the development of procedural knowledge, which may in turn become automatized given sufficient practice opportunities. Such theories posit that these processes apply to the development of a wide range of skills, including L2 learning. Optimizing not only the nature but also the sequence and spacing of practice is therefore critical for efficient learning.

Studies from cognitive psychology have consistently demonstrated that temporally spacing practice sessions leads to better learning and retention than massing practice into a single session (for a review, see Cepeda et al., 2006); the so-called spacing effect. Of even greater relevance to the instructed classroom context, where instruction tends to be interspersed over days or weeks, is the question of whether varying the spacing (i.e., amount of time) between practice sessions also affects how well and for how long learnt information is remembered; the so-called lag effect. The time between practice sessions is known as the intersession interval (ISI). A comprehensive comparison of multiple ISIs by Cepeda et al. (2008) revealed an interdependence between the optimal ISI and the amount of time between the final practice session and the testing time, which is known as the retention interval (RI). They demonstrated that for the learning of trivia facts, as the RI increased, the optimal ISI also increased. For example, for a RI of 7 days, the optimal ISI was 3 days, whereas for a RI of 35 days, the optimal ISI was 8 days. The optimal spacing of practice sessions, therefore, seems to be dependent upon when the learnt knowledge will be needed (e.g., in testing or use).

Numerous theoretical accounts have been proposed to explain the findings that (a) spacing practice is beneficial for learning, and (b) provision of longer spacing between practice sessions leads to better knowledge retention (Toppino & Bloom, 2002). Study-phase retrieval (Toppino & Bloom, 2002) and reminding (Benjamin & Tullis, 2010) accounts propose that successful retrieval of a previously learnt item at a later time point will serve to strengthen the representation of that item, particularly when successful reminding or retrieval occurs after a “high degree of forgetting or a low amount of reminding” (Benjamin & Tullis, 2010, p. 239).

Encoding variability accounts (Benjamin & Tullis, 2010) posit that it is not only the fact of having multiple retrieval opportunities that is important, but also the nature of the retrieval that occurs. Such accounts propose that environmental and contextual differences between practice sessions will result in each occurrence of a learning item being encoded differently, resulting in multiple effective retrieval routes. Similarly, the notion of transfer-appropriate processing suggests that providing multiple, varied practice opportunities will enable the learner to generate “richer, more contextualized representations of the learned material” (Lightbown, 2008, p. 38). An item encountered in a range of contexts is likely to have multiple associations, which will facilitate retrieval across different contexts. Such proposals tie into the concept of providing “desirable difficulty” (Bjork & Bjork, 2014, p. 58) in practice activities and sessions, in order to bring about deeper processing of target items and subsequently better learning. Bjork and Bjork propose that creating situations in which the learner has to work harder to retrieve information from long-term memory (e.g., through distributing practice sessions, varying practice contexts, and introducing contextual interference) will ultimately result in better long-term retention.

These accounts provide complementary interpretations for the general finding that allowing spacing between practice sessions improves learning and retention of target items and further that the amount of time between sessions should be balanced to create effortful retrieval, whilst limiting the likelihood of unsuccessful retrieval or complete forgetting. The question remains, however, as to the relevance of lag effects for L2 grammar learning.

Distribution of Practice Effects for L2 Grammar Learning

The investigation of lag effects (i.e., comparisons of two or more practice distributions varying in length) has been the focus of a small but growing number of SLA studies. Studies have begun to explore lag effects for L2 grammar learning (e.g., Bird, 2010; Rogers, 2015; Suzuki, 2017; Suzuki & DeKeyser, 2017a) and vocabulary learning (e.g., Nakata, 2015; Serrano & Huang, 2018), as well as general L2 proficiency in intensive versus extensive instructional programmes (e.g., Collins & White, 2011; Serrano & Muñoz, 2007).

The four studies most relevant to the current study (on L2 grammar learning in FL contexts) have yielded conflicting results. See Appendix A for a detailed tabular overview of their designs and findings. Bird (2010) observed superior learning under a 14-day ISI than a 3-day ISI condition for L1 Malay learners of the L2 English tense and aspect system, when measured on a written error-correction task at delayed posttest (60-day RI). Similarly demonstrating benefits for spacing that is longer than 2–3 days, Rogers (2015) observed for L1 Arabic learners of L2 English cleft syntactic structures that a 7-day ISI led to superior performance at delayed posttest (42-day RI) than a 2.25-day ISI on a written grammaticality judgement test.

In contrast, Suzuki and DeKeyser (2017a) and Suzuki (2017) found some benefits for spacing that was shorter than 7 days. Suzuki and DeKeyser examined the learning of L2 Japanese present progressive verb morphology under a 1-day and 7-day ISI. They observed an advantage for the shorter ISI, in terms of response speed on an oral picture-description task at delayed posttest (28-day RI). Extending these findings, Suzuki (2017) observed superior gains in accuracy on an oral production task for a 3.3-day group compared to a 7-day group at delayed posttest (28-day RI) for the learning of simple and complex morphology within an artificial language system.

As Appendix A illustrates, there was substantial variation between the studies, which may to some extent account for the difference in findings. The studies utilized different interventions (varying in type and amount), outcome measures, and language features (though Suzuki, 2017, was a conceptual replication of Suzuki & DeKeyser, 2017a). Differences in treatment and task complexity (Donovan & Radosevich, 1999) and the type of knowledge trained and elicited (Suzuki & DeKeyser, 2017a) may have contributed to the contradictory findings. In addition, each study utilized a slightly different set of ISIs and RIs, which, as described previously, can impact test results (Cepeda et al., 2008). Further, the participants in Bird (2010) and Rogers (2015) were identified as intermediate-level learners but as beginners in Suzuki & DeKeyser (2017a) and Suzuki (2017). Practice distribution effects may manifest differently at different proficiencies, with, say, shorter spacing being more helpful among beginner learners or, more generally, lag effects being more difficult to observe at lower proficiencies. It is also important to note that the larger ISI conditions in these studies distributed practice sessions over a longer period of time than the shorter ISI conditions; for example, four sessions over 4 weeks (7-day ISI) versus four sessions over 2 weeks (3.3-day ISI) in Suzuki (2017). In the FL classroom, however, teachers are unable to extend overall teaching time; therefore, a more relevant question concerns how practice can be optimally distributed within the specified curriculum time.

The conflicting findings highlight that lag effects for L2 grammar learning may be influenced by a number of factors, including the amount and nature of training, the nature and modality of testing tasks, the nature of knowledge, and individual learner characteristics. Further research is needed to paint a clearer picture of the role of lag effects for different types of learners engaging in different types of L2 grammar practice.

Distribution of Practice and Child L2 Learning

Critically, it is also important to note that the four studies mentioned previously were conducted with similar learner populations (i.e., adult, university-based learners) and Cepeda et al. (2006) noted that 85% of the studies in their meta-analysis were conducted with adults. Whilst there is emerging evidence that younger learners can benefit from focussed, explicit practice in particular language features (e.g., Kasprowicz & Marsden, 2018; Lichtman, 2016), explicit learning by younger learners tends to be slower than for older, more cognitively mature learners. Further, younger learners’ cognitive abilities (e.g., LAA, working memory) are still developing, which may affect the extent to which they are able to store, access, retain, and recall target knowledge over distributed practice schedules. An as yet underexplored question therefore relates to the role of lag effects for L2 learning by younger learners.

A small number of studies have found advantages for distributed practice over massed practice for language learning among children (e.g., Fishman, Keller, & Atkinson, 1968; Lotfolahi & Salehi, 2016). Additionally, some research (e.g., Collins et al., 1999; Collins & White, 2011) has investigated intensive (5-month) versus more distributed (10-month) language programmes, but as this research was at the programme level and outcomes measures were wide ranging, the findings are less relevant to the rationale for the current study.

In sum, there is a limited amount of research into lag effects with children, particularly studies investigating longer time periods (i.e., ISIs of days or weeks for learning at RIs of weeks or months; Cepeda et al., 2006). Further research is needed to investigate interactions between practice distribution and specific aspects of L2 learning (e.g., grammatical knowledge development), on a range of measures, for young learners. Another issue that has been neglected to date is the potential influence of individual differences on lag effects. We now turn to one such difference, a component of aptitude: LAA.

Language Analytic Ability

LAA can be defined as “the capacity to infer rules of language and make linguistic generalizations or extrapolations” (Skehan, 1998, p. 204). LAA can be further broken down into two subcomponents: grammatical sensitivity (the ability to recognize the grammatical function of words) and inductive learning ability (the ability to infer the grammatical rules governing a set of language; Carroll, 1990; Roehr, 2008), both key to identifying and extrapolating linguistic patterns (Skehan, 2002). Given the emphasis on pattern recognition and application, LAA is thought to be particularly relevant to explicit language learning (DeKeyser, 2012; Robinson, 1997; Roehr, 2008; Skehan, 2002). We would also add deductive language-learning ability to previous models of LAA, the ability to understand a rule and apply it consistently where appropriate. This is likely to be particularly relevant to learning under instruction, where rules are frequently given before practice.

Language Analytic Ability and Lag Effects

To the best of our knowledge, only one study (Suzuki & DeKeyser, 2017b) has investigated the relationship between components of aptitude and learning under different practice distributions. Suzuki and DeKeyser investigated whether LAA and working memory capacity moderated learning under shorter (1-day) and longer (7-day) ISIs. The results of Suzuki and DeKeyser's study indicated a clear interaction between aptitude and treatment for their adult L1 Japanese learners of L2 English, with LAA correlating positively with learning under the longer practice distribution and working memory with learning under the shorter practice distribution. The reasons why LAA played a role in the more distributed practice but not in the less distributed practice, whilst both involved the same grammar instruction, are not clear. As discussed previously, it is hypothesized that distributed practice benefits learning if an individual can recall previously learnt information at the time the practice occurs (Benjamin & Tullis, 2010; Toppino & Bloom, 2002). It may be, then, that higher LAA enables learners to establish more robust or accurate initial knowledge of the target structure, which can then be recalled more successfully in later sessions, thereby allowing learners with high LAA to benefit to a greater extent from longer spacing. Nevertheless, as acknowledged by Suzuki and DeKeyser (2017b), given the limited research in this area such a conclusion is tentative.

Language Analytic Ability and L2 Grammar Learning by Young Learners

Whilst numerous studies (e.g., Erlam, 2005; Li, 2015; Ranta, 2002; Robinson, 1997) have demonstrated that LAA does indeed influence L2 grammar learning by adolescents and adults, the role of LAA for younger learners has received much less attention. DeKeyser (2012) proposed children may rely less on more analytical components of aptitude such as LAA, due to the different learning processes involved in child versus adult learning, with children relying on more implicit learning and older learners on their developing explicit learning abilities (see also Doughty, 2003). Indeed, some findings suggest a much smaller or nonexistent role for LAA among young learners compared to older learners (e.g., DeKeyser, 2000; Harley & Hart, 1998), whereas others have observed that LAA can be predictive of L2 performance by young learners in both immersion classrooms (Ranta, 2002) and naturalistic contexts (Abrahamsson & Hyltenstam, 2008). However, these studies with younger learners have tended to be within naturalistic or immersion settings with sufficiently large amounts of input to facilitate implicit learning processes, leading to a greater reliance on memory-based components of aptitude than on analytical abilities (DeKeyser, 2012). In contrast, younger learners’ ability to draw on implicit learning mechanisms is restricted by the severely limited exposure of FL classrooms (DeKeyser, 2000; Muñoz, 2008). However, there is evidence that with explicit instruction, younger learners can begin to learn explicitly (Kasprowicz & Marsden, 2018; Lichtman, 2016). Therefore, young learners’ developing analytical abilities may play a role in such settings.

Only a handful of studies have investigated LAA and L2 grammar learning by young learners within the instructed FL context (e.g., Hanan, 2015; Kiss & Nikolov, 2005; Muñoz, 2014; Tellier & Roehr–Brackin, 2013, 2017). Tellier and Roehr–Brackin (2013) investigated the relationship between aptitude, measured by the Modern Language Aptitude Test-Elementary (MLAT-E), and L2 French learning by L1-English children aged 8–9. LAA was a significant predictor of L2 proficiency and correlated significantly with gains in grammar knowledge, as well as listening and reading abilities. Similarly, Kiss and Nikolov (2005) found that aptitude, including language analysis and grammatical sensitivity, was the strongest predictor of L2 English proficiency for young L1 Hungarian learners (aged 11 to 12), explaining over 20% of the variation in scores. These studies provide some evidence that LAA can indeed relate to L2 learning for young instructed FL learners.

The current study sought to expand on this research by not only investigating whether LAA moderated L2 grammar learning by young learners but also whether there was a differential effect depending on practice distribution.

Participants

One hundred and thirteen beginner-level L1-English learners of L2 French (aged 8–11) from eight classes across seven primary schools participated in the study (60 boys, 53 girls). Six of the schools were part of one school alliance and were invited to participate following a presentation at a FL teacher-training session. The seventh school was located in the same region. The children had been learning French in school for a minimum of one academic year prior to the study and had minimal access to the language outside of the classroom.

Prior to the current study, French instruction tended to consist of weekly 40- to 60-minute lessons, focussing on learning of key vocabulary, development of comprehension and production skills, and some word-level grammar instruction (e.g., definite and indefinite articles, gender, pronouns, adjective agreement). There is no set scheme of work for UK primary school FL teaching; therefore, the exact content of language lessons in each class varied. Due to the large variation in FL teaching provision across UK primary schools (Tinsley & Board, 2017), this was impossible to avoid. To account for its potentially confounding effect, class was included as a random variable in the analysis (see Analysis section). Additionally, a 4-week preexperimental phase was included. All classes received four lessons introducing the core vocabulary utilized in the main experimental instruction. Each class teacher completed the activities with their class. The researcher observed one preexperimental lesson per class to ensure the materials were delivered consistently across classes.

Intact classes were assigned to the experimental conditions (7-day, 3.5-day, and control group). Random assignment within classes was not possible, due to practical constraints: (a) It was not possible to have learners within the same class completing the training activities at different days or times, and (b) having control and treatment participants within the same class would have increased the likelihood of control participants being exposed to the treatment. The 7-day group included two mixed Year 5/6 classes (ages 9–11) and one Year 5 class (ages 9–10). The 3.5-day group included two Year 5 classes and one Year 4 class (ages 8–9). The control group included one mixed Year 4/5/6 class (ages 8–11) and one mixed Year 5/6 class.

Procedure

A quasi-experimental design was employed. The control group completed the tests only and reverted to their normal French lessons between pre- and posttests. The treatment groups undertook identical tasks, both totalling 180 minutes (see Training section) but differing in the distribution of the sessions (all treatment and testing materials are available on IRIS). The ISIs were 7 days and 3.5 days, in line with Suzuki (2017) and reflecting the most common lesson frequency in UK primary schools (one or two lessons per week). The 7-day group completed three sessions of 60 minutes, each occurring 7 days apart, whereas the 3.5-day group completed six sessions of 30 minutes each occurring 3.5 days apart. Figure 1 illustrates the timing of each testing and training session. The timing of the posttest mirrored the respective ISI for each treatment group (ISI:RI ratio = 100%). The delayed posttest took place exactly 6 weeks after the posttest, giving an RI of 42 days for both groups (ISI:RI ratio = 16.7% for 7-day group, 8.3% for 3.5-day group); the ISI:RI ratio was calculated from the first posttest, rather than the final intervention session, as the first posttest provided an additional opportunity for practice. The 3.5-day group's ISI:RI ratio fell just outside Rohrer and Pashler's (2007) observed optimum of 10 to 30%. The timing was chosen to ensure that all classes could adhere to the schedule, whilst fitting within the constraints of the schools’ timetables and term dates.

Participants were included in the analysis if they had attended all training sessions and both posttests. One 3.5-day participant did not complete the sentence–picture matching pretest, whilst two 7-day participants and four 3.5-day participants did not complete the acceptability judgement test (AJT) pretest. Due to limited time available for testing, a number of participants were unable to complete the AJT at post- and delayed posttest; therefore, the participant number for this task is lower.

Target Feature

The target language system being taught and tested was regular French verb inflections in the present and perfect tenses (Table 1) for first- and third-person singular and plural forms: null (-e), -ons, -ent (present tense number inflections) and ai and a (avoir auxiliaries for the perfect tense), in both oral and written forms. This choice was in line with the curriculum, which states that children should be taught the “conjugation of high frequency verbs” (Department for Education, 2013, p. 2). The participants had not previously received explicit instruction in the features. Such features can be problematic for L2 learners due to an overreliance on lexical items that convey the same semantic information (e.g., subject pronouns indicating person and number; temporal phrases indicating tense), as noted in the Lexical Preference Principle (VanPatten, 2015); see also Marsden (2006) for a study using a similar rationale and Processing Instruction to focus on the same inflectional features with slightly older learners in the same educational context. Additionally, associative theories of learning attribute such difficulties to phenomena such as entrenchment, attention blocking, and overshadowing, which account for effects of (L1) prior experience, salience, and frequency in the input (N. Ellis, 2006).

Training

Training for the 7-day and 3.5-day groups was delivered via a bespoke, digital, game-based application containing a series of mini-games, with each game teaching just one particular grammatical contrast that is expressed by one pair of inflections (e.g., first person singular vs. plural present tense inflections; see Table 1). Training was completed on individual laptops with headphones. All sessions were overseen by the first author; class teachers were present during the training sessions but provided technical support only.

Each mini-game utilized form–meaning mapping activities consisting of brief (approximately 2 minute) explicit information followed by referential reading and listening activities. Referential activities (a component of Processing Instruction; VanPatten, 2015) are input-based tasks that require learners to notice a feature and connect it with a meaning or function to complete the activity. Numerous studies have demonstrated the effectiveness of such activities for L2 morphosyntax (e.g., DeKeyser & Botana, 2015; Kasprowicz & Marsden, 2018; Marsden & Chen, 2011; Shintani, 2015), including Marsden (2006), who also investigated the teaching of French inflectional verb morphology for person, number, and tense among FL learners aged 13–14 years; and McManus and Marsden (2017, 2018, 2019a, 2019b), who investigated the learning of French imparfait. Referential activities make the target grammatical feature task-essential by removing other cues that learners could rely on (e.g., subject pronouns indicating person and number; temporal adverbs indicating tense). For example, in the current study, in mini-game A (first person singular [null] vs. plural [-ons] present tense inflections), a robot described the food that it (je ‘I’) or all the robots (nous ‘we’) liked. The learner chose whether to feed only the robot that spoke or all the robots. After the first set of practice items, the subject pronoun was obscured (by *** in the reading version and by a beep in the listening version). The learners, therefore, had to notice the verb inflection, interpret its number meaning, and feed the correct robot(s). The training utilized 12 regular -er verbs (see Appendix B), which were chosen because they are commonly taught to beginners (e.g., aimer ‘to like’), are cognates of English verbs (e.g., poster ‘to post’), or fit the game context (e.g., surveiller ‘to watch or survey’).

Each mini-game contained three question sets (see Table 1). The first question set included the tutorial (brief explicit information provided alongside the first two question items; see Appendix C). Response options for the tutorial question items were restricted so that the learner had to answer correctly. Learners then completed the main question items. Correct and incorrect responses were indicated aurally by different sounds and visually via the progress bar. Following incorrect answers, learners also received a short explanation (see Appendix D). To successfully complete a question set, the learner had to answer 12 items correctly and received a number of stars upon completion (three stars if all correct, two stars if one incorrect, one star if two incorrect). Learners answered up to two additional questions for each previous item that had been answered incorrectly. If the learner answered three items incorrectly, they lost the question set and had one opportunity to replay the set. After one replay, the learner automatically moved on to the next question set, regardless of their score. The restriction of one replay was included to ensure that all learners had the opportunity to answer all question sets within the time available. These success and replay features also helped to maintain engagement in the game.

Learners completed all three question sets for one mini-game (one grammatical contrast) before moving onto the next mini-game (and next grammatical contrast). The order of mini-games was counterbalanced across the 7-day and 3.5-day groups, with learners either practising present tense inflections before past tense inflections or vice versa. The 3.5-day group completed one mini-game (three question sets) in each session; the 7-day group completed two mini-games (six question sets) in each session. In the final part of the training (second half of session 3 for the 7-day group; session 3b for the 3.5-day group), the learners completed a final additional question set from mini-games A, B, and E, in order to review each of the grammar features.

Test Materials

Learners completed, on laptops, a sentence–picture matching test and an AJT at pre-, post- and delayed posttest. Three versions of each test were created. Each version contained the same number of items, in the same format, and included stimuli created from the same set of lexical items, but with different noun–verb combinations (see Appendix B for the list of verbs included). The three versions were counterbalanced within each experimental group and class, and each learner completed a different version at each time point. Learners (n = 22) of equivalent age and language experience to the main study participants piloted the tests to check the comprehensibility of the instructions, test format, and picture stimuli. Similar tests have been utilized in previous studies with participants of a similar age (e.g., sentence–picture matching task test, Kasprowicz & Marsden, 2018; AJT, Marsden & Chen, 2011).

Analysis

Descriptive statistics (means, standard deviations) of raw scores on each test are provided. Effect sizes (Cohen's d, calculated using the pooled standard deviation) and their confidence intervals (CIs), for comparisons between groups and between time points, are interpreted based on Plonsky and Oswald's (2014) field-specific medians (between-group: small, d = 0.40; medium, d = 0.70; large, d = 1.00; within-group: small, d = 0.60; medium, d = 1.00; large, d = 1.40; p. 889).2 (Additionally, between-group effect sizes corrected for differences at pretest are provided in Appendix E. Although these corrected effect sizes give some descriptive indication of change that takes into account baseline differences, there is unfortunately no known way to date for calculating CIs for these corrected effect sizes, making them inappropriate to interpret within the main article.) Data were nonnormally distributed; therefore, Spearman's rho (including bootstrapped, bias-corrected, 95% CIs) is provided for analysis of the relationship between performance on the outcome measures and (a) the LAA test, and (b) practice accuracy.3 The strength of the relationship indicated by Spearman's rho is interpreted against the following benchmarks: small = 0.25; medium = 0.4; large = 0.6 (Plonsky & Oswald, 2014, p. 889).

To model the effect of the categorical variables group (7-day, 3.5-day, control) and time (pre-, post-, delayed posttest) on test performance, and to account for random effects of learner, class, and question item, the data were analysed via mixed-effects logistic models fit by maximum likelihood with binomial logit functions using the lme4 package in R 3.4.3 (Bates et al., 2015). The data were binary (correct or incorrect). The models included random intercepts to account for variation in average scores by learner, class, and question item. The base model for analysis of each outcome measure can be described as follows:

Influence of Practice Distribution (RQ1)

Influence of Practice Accuracy (RQ1a)

Analysis was conducted to explore the association between the accuracy of learners’ performance during training and subsequent performance at post- and delayed posttest. As the control group did not complete the training activities, it is excluded from this analysis.

Practice Accuracy

The learners’ global practice accuracy score (i.e., percentage of questions answered correctly out of all those attempted across the training sessions) provided an indication of how successfully the learners completed the training.5 The global practice accuracy scores were high for both the 7-day (n = 38, M = 79.6%, CI [76.7%, 82.4%], SD = 8.7%) and 3.5-day groups (n = 41, M = 82.5%, CI [79.9%, 85.2%], SD = 8.4%), with both groups answering more than 75% of practice items correctly on average. The standard deviation and CI around each mean indicate some variation between individuals’ practice scores. The minimum score from any individual within the 7-day group was 62.8% and within the 3.5-day group was 55.4%. An independent samples t-test indicated no significant difference in global practice accuracy between the two groups, t(77) = −1.522, p = .132, d = −0.34, CI [−0.78, 0.11].

To examine the relationship between performance during training sessions (i.e., practice accuracy) and performance on the outcome measures, the models of learners’ performance on each outcome measure were expanded to include practice accuracy (with scores centred on the grand mean to avoid multicollinearity) as a predictor variable:

Influence of Language Analytic Ability (RQ1b)

Although little change was seen in group-level mean scores, the standard deviations (Table 2) suggest substantial within-group variation in performance on each test. We now examine whether this variation can be accounted for by individual differences in LAA.

Language Analytic Ability Test Performance

The descriptive statistics for the three groups’ performance on the LAA test are presented in Table 5. The 3.5-day group's performance was marginally higher than both the 7-day (d = −0.46, CI [−0.90, −0.01]) and control group (d = 0.36, CI [−0.10, 0.81]), although a Kruskal–Wallis test revealed no significant effect of group, ²(2) = 3.607, p = .165. The CIs around the mean overlapped between all three groups, indicating that performance of all three groups fell within a similar range. Notably, the large standard deviations indicate a large amount of within-group variation on this test (Table 5).

Table 5. Performance on Language Analytic Ability Test

Group	n	M (SD)	CIs
7-day	38	26.7 (9.8)	23.4, 29.9
3.5-day	41	30.8 (8.4)	28.2, 33.5
Control	34	27.7 (9.2)	24.5, 30.9

• Note. M = mean; SD = standard deviation; CIs = confidence intervals.

To examine the impact of LAA on performance on the outcome measures, the mixed effects logistic models were expanded to include LAA scores (centred around the grand mean) as a predictor variable6:

Group-Level Performance Compared to Control

Before discussing the findings in relation to the impact of practice distribution, it is necessary to address the (somewhat surprising) finding that, at group-level, minimal differences were observed in the treatment groups’ performances on the outcome measures compared to the control group and, further, that minimal changes over time were observed between time points for all groups. The group-level statistics could suggest minimal learning as a result of the intervention, potentially contrary to much of the existing research on form–meaning mapping (a component of the wider Processing Instruction approach; see DeKeyser & Botana, 2015; and Shintani, 2015 for reviews). For example, Shintani (2015) found a large overall effect (d = 2.60) for Processing Instruction on receptive knowledge at posttest in a meta-analysis of 42 Processing Instruction studies. Additionally, Marsden (2006) used a similar approach to teach a slightly larger grammatical subsystem (inflectional verb morphology for person, number, tense) and found clear learning gains on a battery of measures. However, a number of considerations should be taken into account when interpreting the current findings.

First, in Marsden's (2006) study, the intervention was considerably longer (4.5 hours over 9 weeks) and the learners had experienced on average 200 more hours of French instruction than the learners in the current study. Their slightly higher proficiency likely provided them with a larger and more stable verb lexicon on which to graft an (already) emerging inflectional system (see Marsden & David, 2008, who showed that the size of the verb lexicon correlated positively with inflectional diversity). Also, perhaps critically, the learners were older (aged 13–14 years), thus potentially more able to draw on their explicit inductive and deductive learning mechanisms. Relatedly, it is also possible that, for (at least some of) the young learners in the present study, the intervention may have been too brief, as explicit learning by young learners is slower than for more cognitively mature, older learners (Lichtman, 2016). Indeed, Kasprowicz and Marsden (2018) observed substantial learning gains following form–meaning mapping practice for 9- to 11-year olds, but after a longer (250 minutes) intervention over 5 weeks, which focussed on one grammatical function (subject or object assignment via German definite articles).

Second, there was a high level of within-group variation in the learners’ global practice accuracy scores on the training and in their performance on the outcome measures. This indicated differential benefits of the intervention for individual learners and variation between individuals’ success completing the tests. This is discussed further in light of the findings of the LAA analysis.

Third, the analysis of the learners’ performance in the training sessions revealed a high level of accuracy during the practice activities in both groups, indicating that the learners were attending to and correctly applying the grammatical rules. It would seem, then, that the 7-day and 3.5-day learners’ performance at the posttests (at least at a group level) did not reflect the knowledge being developed during the training sessions. One possible explanation for this discrepancy may be differences between the training and testing activities. Transfer-appropriate processing accounts predict greater success at retrieving previously learnt information when the learning and testing tasks draw on similar processes, skills, and contexts (Lightbown, 2008; Segalowitz, 2003; Spada & Lightbown, 2008). For example, more isolated (decontextualized) instruction may lead to greater gains on explicit, discrete tests, compared to integrated (contextualized) learning activities favouring more communicative tests (Martin–Chang, Levy, & O'Neil, 2007; Spada, Jessop, & Tomita, 2014). Training in the present study involved practice embedded within game-based environments and required repeatedly connecting one inflection, from one particular pair, to a meaning or function in an engaging visual (e.g., robots choosing food in a cafeteria). In contrast, the sentence–picture matching test and AJT required different processes. For example, both tests were in the written modality, in contrast to the training, which had been in both aural and written modalities for each inflection. The matching test required recognition of isolated exemplars, in a decontextualized environment, with two pictures to choose from, and tested all of the target inflections over a small number of items. The AJT G items required learners to recognize correctness and the UG items required them to know that particular features were not grammatical, both knowledge and skills that had not specifically been practiced during the game. Whilst both practice and tests constituted input-based, comprehension activities, the differences in the contexts and actions required may account for why (some of) the learners did not reliably apply knowledge established during the training to the tests.

There are several possible accounts for this, drawing on skill acquisition theory. One is that the learners may not have engaged in transfer-appropriate processing during training and had not acquired representations of the grammatical features that were sufficiently generalizable to the tests. Successfully applying knowledge across different task conditions requires the establishment of relevant and reliable declarative knowledge (as declarative knowledge is transferable to other task conditions and characteristics), yet some learners may not have established either fully accurate or sufficiently robust declarative knowledge for reliable transfer to occur, so recall remained error prone, as is typical of the early stages of skill acquisition. An alternative, though related, explanation might be that during practice, learners established proceduralized and even automatized knowledge of the inflections that was relevant to the game context, but as proceduralized and automatized knowledge is known to be highly specific, this was perhaps not adaptable to the test contexts.

Providing (more) varied practice opportunities may be one way of helping learners consolidate the necessary declarative, proceduralized, and automatized knowledge that is transferable across a wider range of task conditions than found in the current study. This could be built into the current game, as computer delivery provides opportunities to tailor the amount and nature of practice at an individual level (DeKeyser, 2012).

Impact of Practice Distribution

Our analyses of lag effects did not yield convincing evidence that our different practice distributions affected learners’ group performance differentially, at least on the outcome measures utilized here. On the sentence–picture matching test, a small advantage was observed for the 3.5-day group, with group-level improvement between Pre- and Posttest 1 compared to minimal group-level change in the 7-day group's (and control group's) scores over time. Note that the 7-day group's pretest score was higher than that of the 3.5-day group, and therefore the 3.5-day group's gains brought them to a similar level to the 7-day group at posttest. The advantage of the 3.5-day spacing was not maintained at delayed posttest. On the AJT test, no practice distribution effect was observed; neither group showed significant group-level change over time (although there was a small increase in both groups’ scores at posttest on the UG items and for the 7-day group, this was a small effect).

Accuracy during training predicted performance for both groups, on the matching and AJT UG items at posttest. Spearman's rho indicated that the effects were smaller for both groups at delayed posttest, perhaps due to decay of declarative knowledge developed during the training (Suzuki & DeKeyser, 2017a), though a small effect remained for the 3.5-day group. For the AJT G items, accuracy during training was related to posttest performance for the 3.5-day group only. In sum, we observed no clear advantage in knowledge retention for spacing of 3.5 or 7 days on our tests, despite some tentative benefits for the 3.5-day group on a number of findings.

Our tentative finding of some advantage for the 3.5-day group (most clearly, pre- to posttest gains on the sentence–picture matching test) could align with findings by Suzuki and DeKeyser (2017a) and Suzuki (2017), who both observed benefits for spacing that was shorter than 7 days (1 day and 3.3 days respectively), also with beginner learners and focusing on morphology (see also Toppino & Bloom, 2002, for an account of why longer spacing may lead to more forgetting). However, this finding is contrary to Bird (2010) and Rogers (2015), who found advantages for spacing of 7 days or more with intermediate learners. However, when interpreting the relationship between our findings and previous studies we must bear in mind the methodological differences between these studies and our study (see Appendix A). In particular, our study compared less frequent, longer sessions (7-day ISI, 3 × 60 minutes) to more frequent, shorter sessions (3.5-day ISI, 6 × 30 minutes), both distributed over the same period of time (3 weeks); a comparison that is perhaps more reflective of the decisions that teachers have to make regarding how to distribute the curriculum within allocated teaching time. These differences point to the general need for increased replication in our field (as illustrated by Marsden et al., 2018).

Another issue to consider in interpreting the lack of clearer lag effects and the lack of overall gains over time is the mixed findings regarding our instrument reliability (with some data missing and the sentence–picture matching test index falling below the recommended level of acceptability). This could indicate that the tests may not have been able to show robust change over time. However, this concern is mitigated by the fact that sufficient variance and change over time in the scores was observed for both accuracy during training and LAA to be significant predictors of learning, the latter finding to which we now turn.

Impact of Language Analytic Ability

LAA improved the fit of our mixed-effects models and was a significant predictor for both outcome measures, suggesting that LAA significantly influenced learners’ test scores. This finding is consistent with that of Tellier and Roehr–Brackin (2013), who observed that LAA significantly predicted learning for young learners in a classroom context similar to that of the present study. Additionally, the correlations observed between the outcome measures and LAA for the treatment groups (particularly the 3.5-day group and the AJT G and UG items) were similar to the overall association between aptitude and L2 grammar learning (r = .31) observed in Li's (2015) meta-analysis. We found no significant correlations between LAA and pretest scores (see Appendix F).

Considering the explicit nature of the training, which potentially drew on all three constructs elicited by our LAA test (grammatical sensitivity, and deductive and inductive analytic abilities), learners with higher LAA probably better understood the explicit information provided or were more efficient at identifying and applying rules during practice, which in turn led to improved posttest scores, particularly at Posttest 1. This observation aligns with previous studies observing strong relationships between LAA and learning under explicit instruction (e.g., Erlam, 2005; Li, 2015; Robinson, 1997). Further, components of aptitude, such as LAA, have been argued to become increasingly important as tasks increase in complexity and place a higher cognitive burden on the learner (Suzuki & DeKeyser, 2017b). In line with this argument, the significant associations with posttest performance may in part reflect the complexity inherent in transferring knowledge between training and tests, as discussed previously.

Two additional observations merit discussion. First, the correlations with LAA were strongest for the AJT, at least for the 3.5-day group. This is likely due to similarities between the two tests, as both elicited learners’ explicit ability to spot (violations in) patterns. This observation aligns with Granena's (2013) finding that demonstrated a relationship between aptitude tests that draw on more explicit processes and language tasks that encourage analysis of language form.

The second important observation is that LAA was associated more strongly with outcomes for the 3.5-day group than for the 7-day group. Recall that Suzuki and DeKeyser's (2017b) adult learners showed an association between LAA under their longer ISI (7 days), not their shorter ISI (1 day). They argued that learners with higher LAA developed a more accurate and reliable initial understanding of the structures, resulting in more successful retrieval after longer spacing, whereas high LAA was not as important (useful) when practice was repeated just a day later. In contrast, in the present study, stronger associations were, overall, observed between LAA and learning under the shorter distribution. But our shorter distribution was 3.5 days, rather than Suzuki & DeKeyser's 1 day, and our learners were much younger. It is possible that for our learners, the 3.5-day interval showed differential sensitivity to LAA as learning was susceptible, at some level, to the capacity to establish accurate and robust knowledge in the first place, whereas the 7-day interval may have washed out any such sensitivity due to its overall heavier demands on recall. These suggestions are speculative, and further research is needed into how individual differences such as age and, related to age, working memory capacity may interact with distribution of practice effects.

Limitations

Although our study had high ecological validity, this inevitably came with some costs due to practical constraints of carrying out classroom studies, such as participant attrition (with fewer participants completing the AJT test) and the use of intact classes rather than randomization at the individual level. It is also important to acknowledge the brevity of the intervention, which may in part account for the minimal, group-level learning gains. We also note, however, that 180 minutes of instruction focused solely on a subset of inflectional verb morphology over 3 weeks is greater than is likely to occur within the time-limited FL primary school context. Two other limitations of the study are that we have only examined comprehension-based tests (not production) and our sentence–picture matching test had low internal reliability.