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Educational Robotics intervention on Executive Functions in preschool children: A pilot study
Accepted Manuscript Educational Robotics Intervention on Executive Functions in preschool children: a pilot study
Maria Chiara Di Lieto, Emanuela Inguaggiato, Emanuela Castro, Francesca Cecchi, Giovanni Cioni, Marta Dell'Omo, Cecilia Laschi, Chiara Pecini, Giacomo Santerini, Giuseppina Sgandurra, Paolo Dario PII:
S0747-5632(17)30019-5
DOI:
10.1016/j.chb.2017.01.018
Reference:
CHB 4714
To appear in:
Computers in Human Behavior
Received Date:
18 April 2016
Revised Date:
06 January 2017
Accepted Date:
10 January 2017
Please cite this article as: Maria Chiara Di Lieto, Emanuela Inguaggiato, Emanuela Castro, Francesca Cecchi, Giovanni Cioni, Marta Dell'Omo, Cecilia Laschi, Chiara Pecini, Giacomo Santerini, Giuseppina Sgandurra, Paolo Dario, Educational Robotics Intervention on Executive Functions in preschool children: a pilot study, Computers in Human Behavior (2017), doi: 10.1016/j. chb.2017.01.018
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ACCEPTED MANUSCRIPT Highlights 1. Educational Robotics (ER) benefits on Executive Functions in preschool children; 2. Intensive ER training may improve working memory and inhibition skills; 3. This study integrates ER within a theoretical framework of cognitive development.
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Educational Robotics Intervention on Executive Functions in preschool children: a pilot study. Maria Chiara Di Lieto§ Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, Pisa, Italy [email protected] Emanuela Inguaggiato§ Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, Pisa, Italy Scuola Superiore Sant’Anna, Institute of Life of Sciences, Pisa, Italy [email protected] Emanuela Castro The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy [email protected] Francesca Cecchi The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy [email protected] Giovanni Cioni Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, Pisa, Italy Department of Clinical and Experimental Medicine, University of Pisa, Italy [email protected] Marta Dell'Omo Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy [email protected] Cecilia Laschi The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy [email protected] Chiara Pecini Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, Pisa, Italy [email protected] Giacomo Santerini The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy [email protected] Giuseppina Sgandurra Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, Pisa, Italy [email protected]
ACCEPTED MANUSCRIPT Paolo Dario The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy [email protected] §Co-first
authors
Corresponding Author: Prof. Giovanni Cioni, MD Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Viale del Tirreno, 331, 56128, Calambrone, Pisa, Italy [email protected]
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1. Introduction Educational Robotic (ER) is a broad term used to indicate a branch of knowledge requiring students to program robots actions or even to design, create and assembly them. For many years, ER has been using robots as a channel for teaching and learning (Leroux, 1999) and has been applied worldwide to strength specific areas of knowledge and academic skills (Eguchi, 2010; Benitti, 2012; Keren & Fridin, 2014). Some studies (Hussain, Lindh, & Shukur, 2006; Barker & Ansorge, 2007; Nugent, Barker, & Grandgenett, 2008; Nugent, Barker, Grandgenett, & Adamchuk, 2010) have shown that ER has a positive impact on learning, especially in relation to “STEM” areas, such as Science, Technology, Engineering, and Mathematics. In this framework several studies proposed ER activities to improve math achievement. Hussain and collaborators proposed a one-year Lego training to children from 12 to 16 years old: instead of following standard academic tasks, children, working in small groups, used LEGO robots during school activities. The results showed increased math performances in 12-13 years old students that attended the LEGO training in comparison to a passive control group. This finding supports the hypothesis that ER may provide an active learning environment, where children may “construct” also their academic knowledge (Hussain, Lindh, & Shukur, 2006). In another study, Lindh and Holgersson (2007) found a high individual variability in the ER efficacy on math learning: the effects of a one-year robotics training were tested in 11-16 years old students, in comparison to a control group and results showed that the training was effective on problem solving and logical-mathematical skills only for some subgroups of students. La Paglia and co-workers implemented a robotics laboratory for 20 hours during an academic year, involving thirty children from 10 to 12 years old in order to test the ER efficacy on metacognitive skills related to mathematics. Activities required robot construction and programming with tasks of increasing difficulty. Results suggested that, in comparison to a control group, robotics can improve the ability to reflect and monitor during math tasks (La Paglia, Rizzo, & La Barbera, 2011). Even if most of the studies were directed to pre-adolescent age, Bers and colleagues suggest the applicability of robotic activities even in the early childhood (Bers, Flannery, Kazakoff, & Sullivan, 2014). Robotic pets have been utilized into kindergarten classrooms as a tool to enrich the learning environment and in order to promote attention and learning attitude (Yamamoto, Tetsui, Naganuma, & Kimura, 2006). Moreover, rather than being aimed to academic achievement, in pre-school children ER activities are proposed to empower learning prerequisites by encouraging creativity through robot design, stimulating error detection through “debagging” of the robot program and promoting problem-solving representations trough actions sequence planning. In a recent study, fifty-three children from kindergarten took part to a robotics course, called The Tangible K Robotics Program, where the activities involved building and programming robots in order to get particular goals. Child’s computational thinking,
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assessed by a Likert scale measuring analytic and problem-solving skills, improved after the training, suggesting that ER can be useful in empowering learning prerequisites (Bers, Flannery, Kazakoff, & Sullivan, 2014). Kazakoff and collaborators implemented a 1-week intensive robotic laboratory with twenty-seven kindergarten children. A significant increase was found in the experimental group, in comparison to the control group, in sequencing skills after the laboratory (Kazakoff, Sullivan, &Bers, 2013). Taken together, the existing literature suggests that ER may be a tool to increase problem solving skills, cognitive flexibility and metacognition in the early and late childhood (Sullivan, 2008; Mioduser & Levy, 2010). Indeed, the theoretical background of the mentioned studies was based on the constructivism theory: educators claim that robotic “hands-on” experimentation facilitates the transformation of abstract concepts into concrete and verifiable operations, promoting new perspectives for thinking and developing problem-solving skills (Alimisis, 2013; Benitti, 2012). In this sense, an activity with a robot places the child, more than other passive tough technologies, in front of “objects to think with” in challenging tasks and unusual contexts (Papert, 1980). Moreover, ER activities are conducted in a group setting, encouraging cooperation, team working and social learning. Thus, it may be hypothesized that ER has effects on both decision making, top-down cognitive control, metacognition on one side and socialization and team working on the other side. Nevertheless, the ER literature lacks of reliable study designs as control groups are often missing, several variables are simultaneously manipulated, outcome measures tended to be based on observational and qualitative data, age limits of the target population are net clearly defined (Benitti, 2012; Alimisis, 2013). With the aim of conducting more rigorous studies, a better definition of the cognitive construct underlying ER effects is needed and may help in operationalizing the ER outcome measures. According to developmental psychology, problem solving skills, cognitive flexibility and metacognition belong to the cognitive domain of Executive Functions (EFs). Executive functions (also called executive control or cognitive control) refer to a family of adaptive, goal-directed, top-down mental processes needed when you have to focus and pay attention and when an automatic response would be insufficient (Burgess & Simons, 2005; Garon, Bryson, & Smith, 2008). Thanks to EFs people may “mentally playing with ideas, taking the time to think before acting, meeting novel, unanticipated challenges, resisting temptations, and staying focused” (Diamond, 2013). There is general agreement that there are three core EFs (Miyake, Friedman, Emerson, Witzki, Howerter, & Wager, 2000): inhibition (self-control and selective attention), working memory (maintaining and updating information in short term memory), and cognitive flexibility (set shifting, mental flexibility, creativity). According to developmental model of EFs, EF components may have different developmental trajectories: inhibition emerges first during early pre-school years and it is followed by working memory and flexibility (Usai, Viterbori, Traverso, & De
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Franchis, 2013). Early forms of EF, such as self regulation, are predictive of individual differences in late adolescent EFs (Friedman, Miyake, Robinson, & Hewitt, 2011). In addition, EF are predictive of several cognitive milestones and school readiness in early childhood (Garon, Bryson, & Smith, 2008; Clark, Pritchard, & Woodward, 2010). In particular, in pre-school children, inhibition was found to predict problem solving abilities (Senn, Espy, & Kaufmann, 2004) and social prerequisite, such as the acquisition of Theory of Mind (Carlson, Moses, & Breton, 2002). Moreover, some aspects of EFs can predict math and literacy skills from kindergarten (Blair & Razza, 2007). For this reason, recent studies have implemented preschool programs, based on paper and pencil exercises to improve inhibition strategies and updating, demonstrating the usefulness of these types of interventions in the educational field (Diamond, Barnett, Thomas, & Munro, 2007; Kloo&Perner, 2003; Traverso, Viterbori, & Usai, 2015). EF interventions, using software or video games, have the advantage of automatically modifying task difficulty according to performance (auto-adaptive paradigm) and focusing on specific EF components. However, these programs were often rarely generalizable to daily life activities and were resource consuming, requiring individual exercises and extensive teacher training (Bergman Nutley, Söderqvist, Bryde, Thorell, Humphreys, & Klingberg, 2011; Rueda, Checa, & Cómbita, 2012; Rueda, Rothbart, McCandliss, Saccomanno, & Posner, 2005; Thorell, Lindqvist, Bergman Nutley, Bohlin, & Klingberg, 2009). It may be hypothesized that ER allows children to integrate several EF training strengths, such as: i) incremental challenging tasks based on adaptive paradigm, ii) real engaging objects to work on and iii) group settings. Thus, an intensive ER intervention in early childhood may improve the two main preschool EF components, namely working memory and inhibition, through activities that stimulate children to maintain and update information, to inhibit automatic response and to solve problems. This study aimed at responding to the lack of evidence on how robotics can increase learning achievement in students, at an early age, by providing quantitative data on ER impact. In particular, it objectively evaluated, for the first time, the effects of an intensive ER training on EF development in preschool children. To accomplish this, a bee-shaped robot, called Bee-Bot®, was used. The Bee-Bot has won an award for the most impressive hardware for kindergarten and lower primary school children in educational technology (Janka, 2008). The Bee-Bot, a child-friendly device, has been previously used in qualitative studies for teaching visuo-spatial sequencing and distance estimation (Highfield, 2010; Janka, 2008). As Bee-bot is presumed to work mainly within visuo-spatial navigation domain, cognitive evaluation was planned in order to assess not only executive functioning improvement but also more basic visuo-spatial and attention skills.
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2. Materials and method 2.1 Participants Twelve healthy children (7 females; 5 males; age range 5-6 years) attending kindergarten in XXX (XXX) were selected to participate in the Educational Robotics training (ER-Lab). Written consent was obtained from participants’ parents. 2.2 ER Lab intervention The ER-Lab was conducted twice a week for 6 weeks (13 ER training sessions of 75 minutes) using the Bee-Bot robot (Bee-Bot, Campus Store). The design of Bee-Bot is adapted to be child user. The toy has a black/yellow bee shape, is easy to use and handle (Figure 1). The Bee-Bot is capable of storing a series of up to 40 instructions in one programmed sequence. Children can control the Bee-Bot by giving it a sequence of simple instructions for motion or rotation, using seven colourful buttons positioned on its back. In greater detail, there are four orange buttons which move the toy either forward, backward, right or left (90° rotation); a central green button (GO button) which launches the programmed sequence; a blue button to erase memory (CLEAR or X); and another blue button to program a short interruption in the robot motion (PAUSE or II). User cannot modify the length of single step or size of angle rotation; so these parameters are constant and the toy moves for 15 cm in one step and rotate by 90°. At the end of the whole programmed sequence, the toy provides a simple feedback to the user by playing a tune and blinking its eyes. [Insert Figure 1 about here] In the ER-Lab, the children were divided into small groups (three or four children for each group) and each child was provided a Bee-Bot. Group activities and games with the Bee-Bot were programmed in a narrative context, which always differed in each session, in order to stimulate attention, motivation and relational competencies. ER activities proposed during the ER-lab were mainly focused on response inhibition, interference control, working memory and cognitive flexibility. Incremental more difficult activities were proposed allowing the children to gradually achieve a greater competence, with an approach based on the “error-less learning” method (Warmington, Hitch, & Gathercole, 2013). As shown in Table 1, activities were programmed according to difficulty levels.
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The first three sessions were oriented on familiarization of Bee-Bot use. During these sessions, Bee-bot was introduced to the children, explaining programming characteristics, main buttons and the narrative context of activities. Firstly robot programming were proposed, asking the child to move the Bee-Bot in a space, delimited by a carpet, representing a city map, to reach a specific goal closely to the start point. For example: “Bee-Bot has to go to the restaurant on the map, how can it reach the restaurant?”. The activities of the 4th to 7th sessions were directed at working memory, cognitive flexibility and visuospatial planning. The steps of programming and the length of path were progressively incremented, requiring to the child a more complex ability to plan and to visuo-spatial update, e. g. Bee-Bot has to reach the restaurant but first of all it has to reach the gas station on the map. The last six sessions (8th to 13th) were focused on reinforcement of acquired competences and on improving inhibition and interference control. The activities reached their maximum level of complexity, asking to program BeeBot following different and contradictory rules on a more abstract map (not city but a grid with geometric shape different for colors and size). For example, Bee-Bot has to reach the blue circle but it has not to pass on the triangles; or Bee-Bot has to follow the given command if it is written on a green cardboard while had to invert the command if it is written on red cardboard. At the end of each session, children received a star sticker to symbolically reinforce the performed activity. [Insert Table 1 about here] A multidisciplinary team, constituted by psychologists, child neuropsychiatrists and engineers, participated with teachers during sessions to allow for a capitalization of robot potentialities and ER-Lab activities. Engineers showed the robotic platform and its functionalities and potentialities and supported the others for technical and program issues. Psychologists and child neuropsychiatrists’ contributions concerned activities presentation to the teacher and children, monitoring of the ER-Lab and child’s participation. 2.3 Study design According to the Stepped Wedge randomized trial design, children participated in three neuropsychological evaluations performed at regular 6-week intervals: T0, T1 and T2. Between T0 and T1, children did not participate in any training (baseline), while between T1 and T2, ER-Lab intervention was carried out (Figure 2, Flow Diagram ER study). [Insert Figure 2 about here]
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2.4 Outcome measures 2.4.1
Neuropsychological measures
2.4.1.1 Executive Function domain 2.4.1.1.1 Pippo says test (modified version of Simon Says; Marshall and Drew, 2014). This test measured motor inhibition and includes two tasks. In the first task, the child was instructed to perform the action only when the phrase “Pippo says” was issued before a command (activation trial) and to refrain from carrying out the action when this phrase was not stated before a command (inhibition trial). In the second task, an additional difficulty factor was created by the presence of an examiner who performed every action regardless of whether the “Pipposays” command was pronounced or not, while the instruction for the child remain the same as in the first task. 2.4.1.1.2 Backward Corsi Block Tapping subtest (BVN test). This measured visual-spatial working memory abilities and was performed on a standard plastic board, containing 9 blocks of the same colour and material. The examiner touches a sequence of blocks and the backward span was formed by the longest sequence of blocks correctly reproduced by the child in the reverse order with respect to the original order performed by the examiner (Bisiacchi, Cendron, Gugliotta, Tressoldi, & Vio, 2005). 2.4.1.1.3 Inhibition subtest (NEPSY-II test). This evaluated the ability to inhibit automatic responses in favour of novel responses and to switch between response types. It was divided into two conditions: naming and inhibition. Both number of errors and time was computed for each condition (Korkman, Kirk, & Kemp, 2007; Urgesi, Campanella, & Fabbro, 2011). 2.4.1.2 Visuo-spatial domain 2.4.1.2.1 Forward Corsi Block Tapping subtest (BVS test). This measured visual-spatial memory abilities and was performed on a standard plastic board, containing 9 blocks of the same colour and material. The examiner touch a sequence of blocks and the span was formed by the longest sequence of blocks correctly reproduced by the child (Mammarella, Toso, Pazzaglia, & Cornoldi, 2008). 2.4.1.2.2 Route Finding subtest (NEPSY-II test). This evaluated mental navigation by measuring the ability to transfer a route from a simple map to a more complex one. The number of corrected responses was scored (Korkman, Kirk, & Kemp, 2007; Urgesi, Campanella, & Fabbro, 2011). 2.4.1.3 Attention domain 2.4.1.3.1 Attention Sustained subtest (Leiter-R test).
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This measured sustained and selective visual attention. It required the child to cross out one target among a variable number of distractor stimuli. The number of correct targets was scored within a given time limit (Roid & Miller, 2011). 2.4.2 ER-Lab measures 2.4.2.1 ER-Test This was customized ad hoc to quantify the different abilities in Bee-bot programming as shown in Figure 3, the ER test was composed of 9 tasks divided, according to their complexity, into 3 clusters: i) Bee Programming (tasks 1-5), simply assesses Bee button use; ii) Mental Anticipation (tasks 6-8), assesses Bee planning skills in complex visuo-spatial pathways; iii) Inhibition (task 9), assesses inhibition response during Bee navigation. The three clusters were progressively proposed throughout ER-labs. The tests were administered at the beginning of each session. For each task, 0 points were attributed if the target was not reached, 0.5 points if the target was reached with a concrete help (such as using their hand or the Bee-bot to anticipate the correct navigation), 1 point if the target was reached without any concrete help. [Insert Figure 3 about here] 2.4.2.2 ER–questionnaire Two evaluators at the end of each session filled out for each child an ad-hoc created questionnaire. Four social or behavioural abilities were assessed: i) Attention and Motivation (continuous attention during session; active participation; turn respect); ii) Relationship with other children (cooperation); iii) Behavioural control (self-correction, rule respect, frustration tolerance); iv) Inhibition ability (inhibition of automatic responses). For each item, 1 point was attributed if the ability was observed during most of the session time, 0.5 points if was occasionally observed, -1 point if the performance was not observed. All the neuropsychological measures were administered and scored by an assessor blind to the study design.
3. Statistical analysis: Statistical Package for Social Sciences, version 22.0 (IBM SPSS Statistics, IBM Corporation, Armonk, NY) was used for statistical analyses. Non-parametric analyses with raw scores for all tests were performed. Non-parametric comparisons (Wilcoxon test) were planned in order to: i) verify spontaneous
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learning during baseline period, comparing T0 and T1 performances for neuropsychological outcome measures; ii) verify the effect of ER-Lab, comparing T1 and T2 performances for neuropsychological outcome measures; iii) evaluate the capacity in the Bee-bot programming, comparing the average scores at the first two/three sessions with respect to the last two/three sessions for each task cluster (ER-Lab measures). An intention to treat analysis was performed and for missing data, a group average was assigned. Correlations of delta changes in outcome measures (T2-T1) and the ER-test clusters (first three sessions /last three sessions) were checked by Spearman rho non-parametric test for bivariate correlations. For each analysis, significance level was set at p<0.05. In addition, for the ER-Questionnaire percentage of agreement and k value were calculated, as well as the difference for behavioural and social abilities at Wilcoxon test between the first three sessions and last three sessions. 4. Results All participants maintained a high level of motivation during ER-Lab period. Only one child missed the follow-up evaluation (T2) due to sickness. 4.1 Neuropsychological results 4.1.1 Executive Function domain Backward Corsi Block Tapping subtest: a significant difference was found between post vs pre ER-lab training. No significant difference was found during control period (T2-T1: Z=-2.87; p<0.005; T1-T0: Z=-0.30, ns). Pippo Says subtest: no significant differences were found in the first set of the test, while in the second set a significant difference was found only in ER-lab training. No significant difference was found in the control period (second set, T2-T1: Z= -2.14; p<0.05; T1-T0: Z= 1.62; ns). Inhibition subtest (NEPSY-II test): for the inhibition time parameter, a baseline effect, probably due to test-retest, was found in the control period and no difference was detected in the intervention one (T2-T1, Z=-0.59, ns; T1-T0: Z=-3.06, p<0.005). For inhibition accuracy, a significant ER-lab training effect was found (T2-T1: Z=-2.39, p=0.017; T1-T0: Z=-0.65, ns). [Insert Figure 4 about here] 4.1.2 Visuo-spatial Domain No significant differences across the performances at the three time points were found for the Corsi Block Tapping subtest (BVS test) and Route Finding subtest (NEPSY-II test).
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4.1.3 Attention domain Attention Sustained subtest (Leiter-R test): Significant differences were found both in the ER-Lab period (T1-T2: Z=-2.14, p<0.05) and in the control condition (T0-T1: Z=-2.09, p<0.05). 4.2 ER- Test Comparing the last three sessions to the first three sessions, children showed enhanced abilities in the Bee Programming cluster (Z=-3.18, p<0.001) and in Mental Anticipation cluster (Z= -2.59; p=0.010). No significant differences in the Inhibition cluster were found (Z= -0.78; p= ns). Bee Programming and Mental Anticipation clusters were positively correlated with the delta changes in Forward Corsi Block Tapping test (rho=0.48; p=0.048; rho=0.56, p=0.023, respectively). Bee Programming and Mental Anticipation clusters positively correlated with Backword Corsi Block Tapping test (rho=0.53; p=0.031; rho=0.61, p=0.014 respectively). 4.3 ER-Questionnaire The agreement between evaluators reached a percentage of 87.5% with a k value of 0.843 (p<0.0001). The visual inspection analysis of the social and behavioural activities, assessed by ER questionnaire, showed a significant improvement in Behavioural control (p=0.003), Inhibition (p=0.027) and Relationship with other children activities (p=0.013), while Attention and Motivation activities did not show significant changes during the ER-Lab.
5.Discussion In the field of ER literature, this study provides, for the first time, an evidence-based approach and quantitative data for evaluating the effects of an intensive ER-Lab on transversal high-level cognitive functions in preschool children. As recently reported by Benitti and Alimisis (Alimisis, 2013; Benitti, 2012), the most common conclusion in ER studies is to use robotics on middle/high school students to aid academic achievement and comprehension of specific STEM areas. The few studies examining the impact of ER on cognitive functions, such as problem solving and thinking skills, were not conclusive for ER efficacy, as quantitative data were not provided. For this reason, our approach was relevant not only to explore the feasibility of ER in the preschool age but also to measure the effects of ER on those cognitive skills, such as Executive Functions, that may predict future cognitive development and academic achievements (Diamond, Barnett, Thomas, & Munro, 2007). The main result of this study supports the hypothesis that Executive Functions in preschool children may be improved by an intensive, although short, ER training. In our sample, a significant increase, not due to
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test -retest effects, was found in the Executive Function domain and, in particular, in working memory and inhibition skills. According to the theoretical model of Executive Function development, working memory and inhibition represent the two of the main components emerging at an early age (Usai, Viterbori, Traverso, & De Franchis, 2013). Thus, the intervention proposed in this study appears promising for early enhancement of Executive Functions. Programming robot actions requires, for each step, mental anticipation of the action, selection of the appropriate robot command and continuous updating of the programming in order to obtain the goal. This virtuous cycle of cognitive processes may empower planning, inhibition and working memory. By using a navigable robot, such as Bee-bot, the above mentioned effects are particularly evident within the non-verbal Executive Function domain with significant improvement in the active holding of visuo-spatial information in working memory, as measured by Corsi backward and Inhibition tests. A greater effect in Executive Function domain rather than in the “pure” visuo-spatial domain would be expected as ER-Lab intervention works on the abilities to actively manipulate and update visuo-spatial information in memory rather than in more passive processes such as elaboration (e.g. route finding) or maintenance in short term memory (e.g. forward Corsi). In agreement with well-known cognitive models of working memory and executive functions (Mammarella, Toso, Pazzaglia, & Cornoldi, 2008), we hypothesized that ER activities stressed active manipulation of the information held in memory while some pre-post measures, mainly focused on more simple passive maintenance, were used to evaluate possible generalization effects that were not found in our study. As children were asked to fulfill a goal, finding new solutions to solve problem without timelimitations or to generate new paths in the space on the basis on a given rule, ER activities worked mainly on strategic and active component of working memory and inhibition, rather than on speed of processing (as measured by inhibition time parameter) or passive component of visuo-spatial elaboration (assessed by Corsi Block forward and Route Finding subtests). Moreover, robots give concrete feedback, imposing, more than electronic or social games, rules and inhibition of impulsive behaviour. Indeed, starting from the initial ER-lab sessions, children participating in this study have qualitatively increasing learned to wait and check Bee-bot moves and goals before relying on their own behavioural control. Changes in performances of the attention domain were found both during the ER-Lab and the control period, thus they were not interpretable as due to test-retest significant effects. Given the predictive value of preschool Executive Functioning on social and cognitive development (Diamond, Barnett, Thomas, & Munro, 2007; Traverso,Viterbori, & Usai, 2015), these data provide scientific support to the idea that it is possible to quickly improve in 5-year-olds the ability to plan and control progressively more complex navigation tasks. For this reason, ER may be suitable in fostering several essential life skills (cognitive and personal development and team work) through which children
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can develop their potential to use their imagination and to express themselves in school and social activities (Alimisis, 2013). It is reasonable to hypothesize that the above mentioned results were obtained because the ER program, in agreement with the literature on cognitive interventions, had incremental more difficult and more intensive requests, two characteristics considered crucial in obtaining important benefits during cognitive training (Klingberg, Forssberg, & Westerberg, 2002). In addition, in order to maintain a high level of motivation, care was also given to overcome traditional approaches to robotics by introducing them in a ludic or narrative context, such as story-telling (Alimisis, 2013; Fridin, 2014) and in small-group setting to promote social interaction and cooperation (Denis & Hubert, 2001). Furthermore, the assessment of “on-line” difficulty levels in Bee Bot programming, conducted following pre-post assessment at the beginning of each session, showed that children constantly improved their ability in Bee Bot Managing and Mental Anticipation tasks. Children appeared to pass first through a phase of “concretism”, as they initially needed the robot to support movement programming, in order to reach abstraction, where they were then able to achieve mentalization of each Bee-bot action without any kind of concrete support, utilizing also mental rotation (during right or left movements). Although these data may not be sufficient to judge the project efficacy, as they come only from qualitative observations, they may represent a method, suitable also for teachers, to monitor individual abilities to learn robotic programming in early childhood. This research should be considered only a pilot study because of the small sample number and need for further follow-up data. Nevertheless, these preliminary results appear promising and suggest a possible application in children with special needs where empowerment of EFs may have positive influences on several domains (Di Lieto et al., in press). Moreover, this study takes advantage of a multidisciplinary team approach with potential benefits for each involved stakeholder. It was mainly interesting, for the engineers to find a sharable technological language with the other team members in order to show the potentialities of the robots, for the psychologists and child neuropsychiatrists to plan the ER activities in order to promote the EFs and for the teachers to apply with the help and supervision of the other members this new educational approach. ER needs such an approach in order to align robotic technology with learning theories, such as constructivism and constructionism (Papert, 1980; Piaget & Inhelder, 1966), taking advantages from the different background of the experimenter, thus promoting cooperation and networking between researchers (Alimisis, 2013). With these remarks, ER could become an educational tool for promoting EFs.
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Figure Caption Figure 1: A detail of buttons positioned on Bee-Bot’s back during the ER-Lab session. Figure 2: CONSORT Flow Diagram of ER-Study Figure 3: ER-Test tasks: 1-5 Programming Tasks, 6-8 Mental Anticipation Tasks, 9 Inhibition Task. Figure 4: Representative results obtained at T0-T1 and T2 divided according to 3 explored domains: aExecutive Function b- Visuo-spatial, c- Attention. Significance level (*) p< .05
ACCEPTED MANUSCRIPT Acknowledgements. We would like to thank the children and parents who participated in this study and the teachers and director of the “Haring” kindergarten in Pisa (Italy) for allowing us to carry out this study and the Telecom Foundation for its support of the "e-ROB Project" (aimed at creating a platform for Educational Robotics through e-learning). Thanks to Vincent Corsentino for reviewing English of the manuscript.
ACCEPTED MANUSCRIPT Table 1: ER-Lab activities programmed according to difficulty level Sessions
Aim
Activities
1-3
Familiarisation with Bee-bot
Instructions about Bee-bot components and use
4-7
Visuo-spatial planning
Exercises on working memory, cognitive flexibility and visuo-spatial planning and feedback use.
8-13
Inhibition
Exercises on automatic responses, inhibition and divided attention.
Maria Chiara Di Lieto, Emanuela Inguaggiato, Emanuela Castro, Francesca Cecchi, Giovanni Cioni, Marta Dell'Omo, Cecilia Laschi, Chiara Pecini, Giacomo Santerini, Giuseppina Sgandurra, Paolo Dario PII:
S0747-5632(17)30019-5
DOI:
10.1016/j.chb.2017.01.018
Reference:
CHB 4714
To appear in:
Computers in Human Behavior
Received Date:
18 April 2016
Revised Date:
06 January 2017
Accepted Date:
10 January 2017
Please cite this article as: Maria Chiara Di Lieto, Emanuela Inguaggiato, Emanuela Castro, Francesca Cecchi, Giovanni Cioni, Marta Dell'Omo, Cecilia Laschi, Chiara Pecini, Giacomo Santerini, Giuseppina Sgandurra, Paolo Dario, Educational Robotics Intervention on Executive Functions in preschool children: a pilot study, Computers in Human Behavior (2017), doi: 10.1016/j. chb.2017.01.018
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Highlights 1. Educational Robotics (ER) benefits on Executive Functions in preschool children; 2. Intensive ER training may improve working memory and inhibition skills; 3. This study integrates ER within a theoretical framework of cognitive development.
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Educational Robotics Intervention on Executive Functions in preschool children: a pilot study. Maria Chiara Di Lieto§ Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, Pisa, Italy [email protected] Emanuela Inguaggiato§ Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, Pisa, Italy Scuola Superiore Sant’Anna, Institute of Life of Sciences, Pisa, Italy [email protected] Emanuela Castro The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy [email protected] Francesca Cecchi The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy [email protected] Giovanni Cioni Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, Pisa, Italy Department of Clinical and Experimental Medicine, University of Pisa, Italy [email protected] Marta Dell'Omo Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy [email protected] Cecilia Laschi The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy [email protected] Chiara Pecini Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, Pisa, Italy [email protected] Giacomo Santerini The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy [email protected] Giuseppina Sgandurra Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, Pisa, Italy [email protected]
ACCEPTED MANUSCRIPT Paolo Dario The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy [email protected] §Co-first
authors
Corresponding Author: Prof. Giovanni Cioni, MD Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Viale del Tirreno, 331, 56128, Calambrone, Pisa, Italy [email protected]
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1. Introduction Educational Robotic (ER) is a broad term used to indicate a branch of knowledge requiring students to program robots actions or even to design, create and assembly them. For many years, ER has been using robots as a channel for teaching and learning (Leroux, 1999) and has been applied worldwide to strength specific areas of knowledge and academic skills (Eguchi, 2010; Benitti, 2012; Keren & Fridin, 2014). Some studies (Hussain, Lindh, & Shukur, 2006; Barker & Ansorge, 2007; Nugent, Barker, & Grandgenett, 2008; Nugent, Barker, Grandgenett, & Adamchuk, 2010) have shown that ER has a positive impact on learning, especially in relation to “STEM” areas, such as Science, Technology, Engineering, and Mathematics. In this framework several studies proposed ER activities to improve math achievement. Hussain and collaborators proposed a one-year Lego training to children from 12 to 16 years old: instead of following standard academic tasks, children, working in small groups, used LEGO robots during school activities. The results showed increased math performances in 12-13 years old students that attended the LEGO training in comparison to a passive control group. This finding supports the hypothesis that ER may provide an active learning environment, where children may “construct” also their academic knowledge (Hussain, Lindh, & Shukur, 2006). In another study, Lindh and Holgersson (2007) found a high individual variability in the ER efficacy on math learning: the effects of a one-year robotics training were tested in 11-16 years old students, in comparison to a control group and results showed that the training was effective on problem solving and logical-mathematical skills only for some subgroups of students. La Paglia and co-workers implemented a robotics laboratory for 20 hours during an academic year, involving thirty children from 10 to 12 years old in order to test the ER efficacy on metacognitive skills related to mathematics. Activities required robot construction and programming with tasks of increasing difficulty. Results suggested that, in comparison to a control group, robotics can improve the ability to reflect and monitor during math tasks (La Paglia, Rizzo, & La Barbera, 2011). Even if most of the studies were directed to pre-adolescent age, Bers and colleagues suggest the applicability of robotic activities even in the early childhood (Bers, Flannery, Kazakoff, & Sullivan, 2014). Robotic pets have been utilized into kindergarten classrooms as a tool to enrich the learning environment and in order to promote attention and learning attitude (Yamamoto, Tetsui, Naganuma, & Kimura, 2006). Moreover, rather than being aimed to academic achievement, in pre-school children ER activities are proposed to empower learning prerequisites by encouraging creativity through robot design, stimulating error detection through “debagging” of the robot program and promoting problem-solving representations trough actions sequence planning. In a recent study, fifty-three children from kindergarten took part to a robotics course, called The Tangible K Robotics Program, where the activities involved building and programming robots in order to get particular goals. Child’s computational thinking,
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assessed by a Likert scale measuring analytic and problem-solving skills, improved after the training, suggesting that ER can be useful in empowering learning prerequisites (Bers, Flannery, Kazakoff, & Sullivan, 2014). Kazakoff and collaborators implemented a 1-week intensive robotic laboratory with twenty-seven kindergarten children. A significant increase was found in the experimental group, in comparison to the control group, in sequencing skills after the laboratory (Kazakoff, Sullivan, &Bers, 2013). Taken together, the existing literature suggests that ER may be a tool to increase problem solving skills, cognitive flexibility and metacognition in the early and late childhood (Sullivan, 2008; Mioduser & Levy, 2010). Indeed, the theoretical background of the mentioned studies was based on the constructivism theory: educators claim that robotic “hands-on” experimentation facilitates the transformation of abstract concepts into concrete and verifiable operations, promoting new perspectives for thinking and developing problem-solving skills (Alimisis, 2013; Benitti, 2012). In this sense, an activity with a robot places the child, more than other passive tough technologies, in front of “objects to think with” in challenging tasks and unusual contexts (Papert, 1980). Moreover, ER activities are conducted in a group setting, encouraging cooperation, team working and social learning. Thus, it may be hypothesized that ER has effects on both decision making, top-down cognitive control, metacognition on one side and socialization and team working on the other side. Nevertheless, the ER literature lacks of reliable study designs as control groups are often missing, several variables are simultaneously manipulated, outcome measures tended to be based on observational and qualitative data, age limits of the target population are net clearly defined (Benitti, 2012; Alimisis, 2013). With the aim of conducting more rigorous studies, a better definition of the cognitive construct underlying ER effects is needed and may help in operationalizing the ER outcome measures. According to developmental psychology, problem solving skills, cognitive flexibility and metacognition belong to the cognitive domain of Executive Functions (EFs). Executive functions (also called executive control or cognitive control) refer to a family of adaptive, goal-directed, top-down mental processes needed when you have to focus and pay attention and when an automatic response would be insufficient (Burgess & Simons, 2005; Garon, Bryson, & Smith, 2008). Thanks to EFs people may “mentally playing with ideas, taking the time to think before acting, meeting novel, unanticipated challenges, resisting temptations, and staying focused” (Diamond, 2013). There is general agreement that there are three core EFs (Miyake, Friedman, Emerson, Witzki, Howerter, & Wager, 2000): inhibition (self-control and selective attention), working memory (maintaining and updating information in short term memory), and cognitive flexibility (set shifting, mental flexibility, creativity). According to developmental model of EFs, EF components may have different developmental trajectories: inhibition emerges first during early pre-school years and it is followed by working memory and flexibility (Usai, Viterbori, Traverso, & De
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Franchis, 2013). Early forms of EF, such as self regulation, are predictive of individual differences in late adolescent EFs (Friedman, Miyake, Robinson, & Hewitt, 2011). In addition, EF are predictive of several cognitive milestones and school readiness in early childhood (Garon, Bryson, & Smith, 2008; Clark, Pritchard, & Woodward, 2010). In particular, in pre-school children, inhibition was found to predict problem solving abilities (Senn, Espy, & Kaufmann, 2004) and social prerequisite, such as the acquisition of Theory of Mind (Carlson, Moses, & Breton, 2002). Moreover, some aspects of EFs can predict math and literacy skills from kindergarten (Blair & Razza, 2007). For this reason, recent studies have implemented preschool programs, based on paper and pencil exercises to improve inhibition strategies and updating, demonstrating the usefulness of these types of interventions in the educational field (Diamond, Barnett, Thomas, & Munro, 2007; Kloo&Perner, 2003; Traverso, Viterbori, & Usai, 2015). EF interventions, using software or video games, have the advantage of automatically modifying task difficulty according to performance (auto-adaptive paradigm) and focusing on specific EF components. However, these programs were often rarely generalizable to daily life activities and were resource consuming, requiring individual exercises and extensive teacher training (Bergman Nutley, Söderqvist, Bryde, Thorell, Humphreys, & Klingberg, 2011; Rueda, Checa, & Cómbita, 2012; Rueda, Rothbart, McCandliss, Saccomanno, & Posner, 2005; Thorell, Lindqvist, Bergman Nutley, Bohlin, & Klingberg, 2009). It may be hypothesized that ER allows children to integrate several EF training strengths, such as: i) incremental challenging tasks based on adaptive paradigm, ii) real engaging objects to work on and iii) group settings. Thus, an intensive ER intervention in early childhood may improve the two main preschool EF components, namely working memory and inhibition, through activities that stimulate children to maintain and update information, to inhibit automatic response and to solve problems. This study aimed at responding to the lack of evidence on how robotics can increase learning achievement in students, at an early age, by providing quantitative data on ER impact. In particular, it objectively evaluated, for the first time, the effects of an intensive ER training on EF development in preschool children. To accomplish this, a bee-shaped robot, called Bee-Bot®, was used. The Bee-Bot has won an award for the most impressive hardware for kindergarten and lower primary school children in educational technology (Janka, 2008). The Bee-Bot, a child-friendly device, has been previously used in qualitative studies for teaching visuo-spatial sequencing and distance estimation (Highfield, 2010; Janka, 2008). As Bee-bot is presumed to work mainly within visuo-spatial navigation domain, cognitive evaluation was planned in order to assess not only executive functioning improvement but also more basic visuo-spatial and attention skills.
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2. Materials and method 2.1 Participants Twelve healthy children (7 females; 5 males; age range 5-6 years) attending kindergarten in XXX (XXX) were selected to participate in the Educational Robotics training (ER-Lab). Written consent was obtained from participants’ parents. 2.2 ER Lab intervention The ER-Lab was conducted twice a week for 6 weeks (13 ER training sessions of 75 minutes) using the Bee-Bot robot (Bee-Bot, Campus Store). The design of Bee-Bot is adapted to be child user. The toy has a black/yellow bee shape, is easy to use and handle (Figure 1). The Bee-Bot is capable of storing a series of up to 40 instructions in one programmed sequence. Children can control the Bee-Bot by giving it a sequence of simple instructions for motion or rotation, using seven colourful buttons positioned on its back. In greater detail, there are four orange buttons which move the toy either forward, backward, right or left (90° rotation); a central green button (GO button) which launches the programmed sequence; a blue button to erase memory (CLEAR or X); and another blue button to program a short interruption in the robot motion (PAUSE or II). User cannot modify the length of single step or size of angle rotation; so these parameters are constant and the toy moves for 15 cm in one step and rotate by 90°. At the end of the whole programmed sequence, the toy provides a simple feedback to the user by playing a tune and blinking its eyes. [Insert Figure 1 about here] In the ER-Lab, the children were divided into small groups (three or four children for each group) and each child was provided a Bee-Bot. Group activities and games with the Bee-Bot were programmed in a narrative context, which always differed in each session, in order to stimulate attention, motivation and relational competencies. ER activities proposed during the ER-lab were mainly focused on response inhibition, interference control, working memory and cognitive flexibility. Incremental more difficult activities were proposed allowing the children to gradually achieve a greater competence, with an approach based on the “error-less learning” method (Warmington, Hitch, & Gathercole, 2013). As shown in Table 1, activities were programmed according to difficulty levels.
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The first three sessions were oriented on familiarization of Bee-Bot use. During these sessions, Bee-bot was introduced to the children, explaining programming characteristics, main buttons and the narrative context of activities. Firstly robot programming were proposed, asking the child to move the Bee-Bot in a space, delimited by a carpet, representing a city map, to reach a specific goal closely to the start point. For example: “Bee-Bot has to go to the restaurant on the map, how can it reach the restaurant?”. The activities of the 4th to 7th sessions were directed at working memory, cognitive flexibility and visuospatial planning. The steps of programming and the length of path were progressively incremented, requiring to the child a more complex ability to plan and to visuo-spatial update, e. g. Bee-Bot has to reach the restaurant but first of all it has to reach the gas station on the map. The last six sessions (8th to 13th) were focused on reinforcement of acquired competences and on improving inhibition and interference control. The activities reached their maximum level of complexity, asking to program BeeBot following different and contradictory rules on a more abstract map (not city but a grid with geometric shape different for colors and size). For example, Bee-Bot has to reach the blue circle but it has not to pass on the triangles; or Bee-Bot has to follow the given command if it is written on a green cardboard while had to invert the command if it is written on red cardboard. At the end of each session, children received a star sticker to symbolically reinforce the performed activity. [Insert Table 1 about here] A multidisciplinary team, constituted by psychologists, child neuropsychiatrists and engineers, participated with teachers during sessions to allow for a capitalization of robot potentialities and ER-Lab activities. Engineers showed the robotic platform and its functionalities and potentialities and supported the others for technical and program issues. Psychologists and child neuropsychiatrists’ contributions concerned activities presentation to the teacher and children, monitoring of the ER-Lab and child’s participation. 2.3 Study design According to the Stepped Wedge randomized trial design, children participated in three neuropsychological evaluations performed at regular 6-week intervals: T0, T1 and T2. Between T0 and T1, children did not participate in any training (baseline), while between T1 and T2, ER-Lab intervention was carried out (Figure 2, Flow Diagram ER study). [Insert Figure 2 about here]
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2.4 Outcome measures 2.4.1
Neuropsychological measures
2.4.1.1 Executive Function domain 2.4.1.1.1 Pippo says test (modified version of Simon Says; Marshall and Drew, 2014). This test measured motor inhibition and includes two tasks. In the first task, the child was instructed to perform the action only when the phrase “Pippo says” was issued before a command (activation trial) and to refrain from carrying out the action when this phrase was not stated before a command (inhibition trial). In the second task, an additional difficulty factor was created by the presence of an examiner who performed every action regardless of whether the “Pipposays” command was pronounced or not, while the instruction for the child remain the same as in the first task. 2.4.1.1.2 Backward Corsi Block Tapping subtest (BVN test). This measured visual-spatial working memory abilities and was performed on a standard plastic board, containing 9 blocks of the same colour and material. The examiner touches a sequence of blocks and the backward span was formed by the longest sequence of blocks correctly reproduced by the child in the reverse order with respect to the original order performed by the examiner (Bisiacchi, Cendron, Gugliotta, Tressoldi, & Vio, 2005). 2.4.1.1.3 Inhibition subtest (NEPSY-II test). This evaluated the ability to inhibit automatic responses in favour of novel responses and to switch between response types. It was divided into two conditions: naming and inhibition. Both number of errors and time was computed for each condition (Korkman, Kirk, & Kemp, 2007; Urgesi, Campanella, & Fabbro, 2011). 2.4.1.2 Visuo-spatial domain 2.4.1.2.1 Forward Corsi Block Tapping subtest (BVS test). This measured visual-spatial memory abilities and was performed on a standard plastic board, containing 9 blocks of the same colour and material. The examiner touch a sequence of blocks and the span was formed by the longest sequence of blocks correctly reproduced by the child (Mammarella, Toso, Pazzaglia, & Cornoldi, 2008). 2.4.1.2.2 Route Finding subtest (NEPSY-II test). This evaluated mental navigation by measuring the ability to transfer a route from a simple map to a more complex one. The number of corrected responses was scored (Korkman, Kirk, & Kemp, 2007; Urgesi, Campanella, & Fabbro, 2011). 2.4.1.3 Attention domain 2.4.1.3.1 Attention Sustained subtest (Leiter-R test).
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This measured sustained and selective visual attention. It required the child to cross out one target among a variable number of distractor stimuli. The number of correct targets was scored within a given time limit (Roid & Miller, 2011). 2.4.2 ER-Lab measures 2.4.2.1 ER-Test This was customized ad hoc to quantify the different abilities in Bee-bot programming as shown in Figure 3, the ER test was composed of 9 tasks divided, according to their complexity, into 3 clusters: i) Bee Programming (tasks 1-5), simply assesses Bee button use; ii) Mental Anticipation (tasks 6-8), assesses Bee planning skills in complex visuo-spatial pathways; iii) Inhibition (task 9), assesses inhibition response during Bee navigation. The three clusters were progressively proposed throughout ER-labs. The tests were administered at the beginning of each session. For each task, 0 points were attributed if the target was not reached, 0.5 points if the target was reached with a concrete help (such as using their hand or the Bee-bot to anticipate the correct navigation), 1 point if the target was reached without any concrete help. [Insert Figure 3 about here] 2.4.2.2 ER–questionnaire Two evaluators at the end of each session filled out for each child an ad-hoc created questionnaire. Four social or behavioural abilities were assessed: i) Attention and Motivation (continuous attention during session; active participation; turn respect); ii) Relationship with other children (cooperation); iii) Behavioural control (self-correction, rule respect, frustration tolerance); iv) Inhibition ability (inhibition of automatic responses). For each item, 1 point was attributed if the ability was observed during most of the session time, 0.5 points if was occasionally observed, -1 point if the performance was not observed. All the neuropsychological measures were administered and scored by an assessor blind to the study design.
3. Statistical analysis: Statistical Package for Social Sciences, version 22.0 (IBM SPSS Statistics, IBM Corporation, Armonk, NY) was used for statistical analyses. Non-parametric analyses with raw scores for all tests were performed. Non-parametric comparisons (Wilcoxon test) were planned in order to: i) verify spontaneous
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learning during baseline period, comparing T0 and T1 performances for neuropsychological outcome measures; ii) verify the effect of ER-Lab, comparing T1 and T2 performances for neuropsychological outcome measures; iii) evaluate the capacity in the Bee-bot programming, comparing the average scores at the first two/three sessions with respect to the last two/three sessions for each task cluster (ER-Lab measures). An intention to treat analysis was performed and for missing data, a group average was assigned. Correlations of delta changes in outcome measures (T2-T1) and the ER-test clusters (first three sessions /last three sessions) were checked by Spearman rho non-parametric test for bivariate correlations. For each analysis, significance level was set at p<0.05. In addition, for the ER-Questionnaire percentage of agreement and k value were calculated, as well as the difference for behavioural and social abilities at Wilcoxon test between the first three sessions and last three sessions. 4. Results All participants maintained a high level of motivation during ER-Lab period. Only one child missed the follow-up evaluation (T2) due to sickness. 4.1 Neuropsychological results 4.1.1 Executive Function domain Backward Corsi Block Tapping subtest: a significant difference was found between post vs pre ER-lab training. No significant difference was found during control period (T2-T1: Z=-2.87; p<0.005; T1-T0: Z=-0.30, ns). Pippo Says subtest: no significant differences were found in the first set of the test, while in the second set a significant difference was found only in ER-lab training. No significant difference was found in the control period (second set, T2-T1: Z= -2.14; p<0.05; T1-T0: Z= 1.62; ns). Inhibition subtest (NEPSY-II test): for the inhibition time parameter, a baseline effect, probably due to test-retest, was found in the control period and no difference was detected in the intervention one (T2-T1, Z=-0.59, ns; T1-T0: Z=-3.06, p<0.005). For inhibition accuracy, a significant ER-lab training effect was found (T2-T1: Z=-2.39, p=0.017; T1-T0: Z=-0.65, ns). [Insert Figure 4 about here] 4.1.2 Visuo-spatial Domain No significant differences across the performances at the three time points were found for the Corsi Block Tapping subtest (BVS test) and Route Finding subtest (NEPSY-II test).
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4.1.3 Attention domain Attention Sustained subtest (Leiter-R test): Significant differences were found both in the ER-Lab period (T1-T2: Z=-2.14, p<0.05) and in the control condition (T0-T1: Z=-2.09, p<0.05). 4.2 ER- Test Comparing the last three sessions to the first three sessions, children showed enhanced abilities in the Bee Programming cluster (Z=-3.18, p<0.001) and in Mental Anticipation cluster (Z= -2.59; p=0.010). No significant differences in the Inhibition cluster were found (Z= -0.78; p= ns). Bee Programming and Mental Anticipation clusters were positively correlated with the delta changes in Forward Corsi Block Tapping test (rho=0.48; p=0.048; rho=0.56, p=0.023, respectively). Bee Programming and Mental Anticipation clusters positively correlated with Backword Corsi Block Tapping test (rho=0.53; p=0.031; rho=0.61, p=0.014 respectively). 4.3 ER-Questionnaire The agreement between evaluators reached a percentage of 87.5% with a k value of 0.843 (p<0.0001). The visual inspection analysis of the social and behavioural activities, assessed by ER questionnaire, showed a significant improvement in Behavioural control (p=0.003), Inhibition (p=0.027) and Relationship with other children activities (p=0.013), while Attention and Motivation activities did not show significant changes during the ER-Lab.
5.Discussion In the field of ER literature, this study provides, for the first time, an evidence-based approach and quantitative data for evaluating the effects of an intensive ER-Lab on transversal high-level cognitive functions in preschool children. As recently reported by Benitti and Alimisis (Alimisis, 2013; Benitti, 2012), the most common conclusion in ER studies is to use robotics on middle/high school students to aid academic achievement and comprehension of specific STEM areas. The few studies examining the impact of ER on cognitive functions, such as problem solving and thinking skills, were not conclusive for ER efficacy, as quantitative data were not provided. For this reason, our approach was relevant not only to explore the feasibility of ER in the preschool age but also to measure the effects of ER on those cognitive skills, such as Executive Functions, that may predict future cognitive development and academic achievements (Diamond, Barnett, Thomas, & Munro, 2007). The main result of this study supports the hypothesis that Executive Functions in preschool children may be improved by an intensive, although short, ER training. In our sample, a significant increase, not due to
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test -retest effects, was found in the Executive Function domain and, in particular, in working memory and inhibition skills. According to the theoretical model of Executive Function development, working memory and inhibition represent the two of the main components emerging at an early age (Usai, Viterbori, Traverso, & De Franchis, 2013). Thus, the intervention proposed in this study appears promising for early enhancement of Executive Functions. Programming robot actions requires, for each step, mental anticipation of the action, selection of the appropriate robot command and continuous updating of the programming in order to obtain the goal. This virtuous cycle of cognitive processes may empower planning, inhibition and working memory. By using a navigable robot, such as Bee-bot, the above mentioned effects are particularly evident within the non-verbal Executive Function domain with significant improvement in the active holding of visuo-spatial information in working memory, as measured by Corsi backward and Inhibition tests. A greater effect in Executive Function domain rather than in the “pure” visuo-spatial domain would be expected as ER-Lab intervention works on the abilities to actively manipulate and update visuo-spatial information in memory rather than in more passive processes such as elaboration (e.g. route finding) or maintenance in short term memory (e.g. forward Corsi). In agreement with well-known cognitive models of working memory and executive functions (Mammarella, Toso, Pazzaglia, & Cornoldi, 2008), we hypothesized that ER activities stressed active manipulation of the information held in memory while some pre-post measures, mainly focused on more simple passive maintenance, were used to evaluate possible generalization effects that were not found in our study. As children were asked to fulfill a goal, finding new solutions to solve problem without timelimitations or to generate new paths in the space on the basis on a given rule, ER activities worked mainly on strategic and active component of working memory and inhibition, rather than on speed of processing (as measured by inhibition time parameter) or passive component of visuo-spatial elaboration (assessed by Corsi Block forward and Route Finding subtests). Moreover, robots give concrete feedback, imposing, more than electronic or social games, rules and inhibition of impulsive behaviour. Indeed, starting from the initial ER-lab sessions, children participating in this study have qualitatively increasing learned to wait and check Bee-bot moves and goals before relying on their own behavioural control. Changes in performances of the attention domain were found both during the ER-Lab and the control period, thus they were not interpretable as due to test-retest significant effects. Given the predictive value of preschool Executive Functioning on social and cognitive development (Diamond, Barnett, Thomas, & Munro, 2007; Traverso,Viterbori, & Usai, 2015), these data provide scientific support to the idea that it is possible to quickly improve in 5-year-olds the ability to plan and control progressively more complex navigation tasks. For this reason, ER may be suitable in fostering several essential life skills (cognitive and personal development and team work) through which children
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can develop their potential to use their imagination and to express themselves in school and social activities (Alimisis, 2013). It is reasonable to hypothesize that the above mentioned results were obtained because the ER program, in agreement with the literature on cognitive interventions, had incremental more difficult and more intensive requests, two characteristics considered crucial in obtaining important benefits during cognitive training (Klingberg, Forssberg, & Westerberg, 2002). In addition, in order to maintain a high level of motivation, care was also given to overcome traditional approaches to robotics by introducing them in a ludic or narrative context, such as story-telling (Alimisis, 2013; Fridin, 2014) and in small-group setting to promote social interaction and cooperation (Denis & Hubert, 2001). Furthermore, the assessment of “on-line” difficulty levels in Bee Bot programming, conducted following pre-post assessment at the beginning of each session, showed that children constantly improved their ability in Bee Bot Managing and Mental Anticipation tasks. Children appeared to pass first through a phase of “concretism”, as they initially needed the robot to support movement programming, in order to reach abstraction, where they were then able to achieve mentalization of each Bee-bot action without any kind of concrete support, utilizing also mental rotation (during right or left movements). Although these data may not be sufficient to judge the project efficacy, as they come only from qualitative observations, they may represent a method, suitable also for teachers, to monitor individual abilities to learn robotic programming in early childhood. This research should be considered only a pilot study because of the small sample number and need for further follow-up data. Nevertheless, these preliminary results appear promising and suggest a possible application in children with special needs where empowerment of EFs may have positive influences on several domains (Di Lieto et al., in press). Moreover, this study takes advantage of a multidisciplinary team approach with potential benefits for each involved stakeholder. It was mainly interesting, for the engineers to find a sharable technological language with the other team members in order to show the potentialities of the robots, for the psychologists and child neuropsychiatrists to plan the ER activities in order to promote the EFs and for the teachers to apply with the help and supervision of the other members this new educational approach. ER needs such an approach in order to align robotic technology with learning theories, such as constructivism and constructionism (Papert, 1980; Piaget & Inhelder, 1966), taking advantages from the different background of the experimenter, thus promoting cooperation and networking between researchers (Alimisis, 2013). With these remarks, ER could become an educational tool for promoting EFs.
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Figure Caption Figure 1: A detail of buttons positioned on Bee-Bot’s back during the ER-Lab session. Figure 2: CONSORT Flow Diagram of ER-Study Figure 3: ER-Test tasks: 1-5 Programming Tasks, 6-8 Mental Anticipation Tasks, 9 Inhibition Task. Figure 4: Representative results obtained at T0-T1 and T2 divided according to 3 explored domains: aExecutive Function b- Visuo-spatial, c- Attention. Significance level (*) p< .05
ACCEPTED MANUSCRIPT Acknowledgements. We would like to thank the children and parents who participated in this study and the teachers and director of the “Haring” kindergarten in Pisa (Italy) for allowing us to carry out this study and the Telecom Foundation for its support of the "e-ROB Project" (aimed at creating a platform for Educational Robotics through e-learning). Thanks to Vincent Corsentino for reviewing English of the manuscript.
ACCEPTED MANUSCRIPT Table 1: ER-Lab activities programmed according to difficulty level Sessions
Aim
Activities
1-3
Familiarisation with Bee-bot
Instructions about Bee-bot components and use
4-7
Visuo-spatial planning
Exercises on working memory, cognitive flexibility and visuo-spatial planning and feedback use.
8-13
Inhibition
Exercises on automatic responses, inhibition and divided attention.