09. Assessment, Evaluation, Testing and Measurement
Paper
Theory-Based Behavioral Indicators for Children’s Purchasing Self-Control in a Computer-Based Simulated Supermarket
Philine Drake1, Johannes Hartig1, Manuel Froitzheim2, Gunnar Mau3, Hanna Schramm-Klein2
1DIPF, Germany; 2School of Economic Disciplines, University of Siegen, Germany; 3Department of Psychology, DHGS German University of Health and Sport, Berlin, Germany
Presenting Author: Drake, Philine
Market demands with their multitude of stimuli and information can be particularly overwhelming for children, whose cognitive abilities and skills are not yet fully developed and who lack market experience and knowledge (Mau et al., 2014). To better understand children’s purchase decisions, associated behaviors, and deficits, Mau et al. (2016) analyzed children’s behaviors in a simulated supermarket environment. They showed that children often behave differently at the point of sale than they intended and expected to when making their purchase decision. Slightly more than half of the children indicated that they would primarily look for low prices when shopping. Although the children in the subsequent observation of their shopping behavior had a limited budget and were tasked with buying the cheapest products, it was found that they clearly tended to select products more based on package design or brand. These findings indicate that children have difficulty implementing a basic requirement of goal-oriented consumer behavior, namely, taking the right actions to achieve a set goal (Bagozzi & Dholakia, 1999).
Regarding the question of how actions in purchasing processes could be implemented in a goal-oriented manner, we draw on basic theoretical frameworks of action regulation that include feedback loops, such as the cybernetic TOTE model (Powers, 1973). Carver and Scheier (1981) assume that the successful realization of a goal state (e.g., fulfillment of the shopping list) occurs by passing through loops in which the existing and target states (e.g., contents of the shopping cart vs. the shopping list) are repeatedly compared with each other and a deviation is successively reduced by operations until the loop is exited. Consequently, the execution of action is expressed as a sequence of corresponding operations and is always in the interplay between the goals of the agent and the situational requirements. According to Gillebaart (2018), setting standards or goals as well as monitoring deviations are aspects of self-regulation, while successful self-control comes into play within the feedback loop in the ‘operate’ phase. Self-control has been described by Baumeister et al. (2007) as the mental processes that enable people to control their thoughts, emotions, and behaviors to achieve higher-level goals. While operating, various aspects of self-control can be observed, such as suppressing the impulse to be tempted by alluring stimuli that are not in line with our long-term goals (e.g., completing the shopping list), avoiding situations that might lead one into temptation (e.g., forgo the candy shelf), or even delaying gratification with an immediate, smaller reward in order to obtain a larger, delayed reward. According to Inzlicht et al. (2014), as a result of repeated self-control efforts, there may be a change in the degree of self-control displayed. This is attributed to a change in task priorities, a shift in motivation away from so-called "have-to" to "want-to" goals that provide more pleasure and satisfaction. Therefore, the process of action regulation is always under the influence of changing motivations and the attendant changes in emotions and attention. Although self-control has been highlighted in its importance for the successful implementation of consumer goals (e.g., by avoiding impulsive purchases; Baumeister, 2002), there is still no study that specifically captures children's operations in the purchasing process and relates the extent of self-controlled behavior to the successful implementation of a purchase intention.
Methodology, Methods, Research Instruments or Sources UsedTo address this gap, we used a computer-based supermarket simulation to study children's shopping behavior at the point of sale. In this task, children were asked to complete a shopping task based on a shopping list in the supermarket simulation. The supermarket simulation is designed so that, at the behavioral level, children's attentional behavior can be inferred from the log data of the computer-based task (Silberer, 2009). Attentional behavior includes observable attention to objects in the store environment, i.e., how often or how long children look at individual products. The supermarket simulation was intentionally designed to include elements that are not required for the performance task. The extent to which children engage with these irrelevant elements can be gauged from their attentional behavior and, at the behavioral level, enables differentiation between actions that are more conducive to have-to goals (as defined by the task) or want-to goals as defined by Inzlicht et al. (2014).
The data analysis focused on whether the covariance among behavioral indicators hypothesized to capture self-control (e.g., the extent of engagement with task-irrelevant products) could be explained by a single common factor and how that factor was related to task success, monitoring of task performance, and spending. A sample of 136 elementary school children was given a shopping list and a limited budget. To extract behavioral indicators from the log data, we used the finite-state machine approach (Kroehne & Goldhammer, 2018).
Conclusions, Expected Outcomes or FindingsA one-dimensional confirmatory factor analysis (CFA) with all assumed indicators was conducted. The model for self-control included four variables: The temporal extent to which children paid attention to irrelevant shelves (S1) or products (S2), the frequency with which they purchased irrelevant products that were not on the shopping list (S3), or visited irrelevant shelves (S4). The model showed a largely good fit (χ2(1) = 2.276, p = .131, RMSEA = 0.105, 90% RMSEA CI [.000, .294], CFI = 0.993, TLI = 0.956, SRMR = .04). Only the RMSEA exceeded the cut-off criterion. Task success was estimated using the partial credit model. The significant correlation between task success (WLE) and the factor for self-control (r(113)=.44, p<.001) indicates that self-control plays an important role in the purchase process. Our results also show that children who monitored their spending (imprecision of estimates, r(108)= -.35, p<.001) and task success (r(111)=.40, p<.001) more carefully tended to show greater self-control in task performance. Our study illustrates how theory-based factors can be extracted from log data of computerized tasks and demonstrates their diagnostic potential, which can be used to improve the quality and richness of psychological and educational assessments.
ReferencesBagozzi, R. P., & Dholakia, U. (1999). Goal Setting and Goal Striving in Consumer Behavior. Journal of Marketing, 63(4), 19-32. https://doi.org/10.1177/00222429990634s104
Baumeister, R. F. (2002). Yielding to temptation: Self-control failure, impulsive purchasing, and consumer behavior. Journal of Consumer Research, 28(4), 670-676. https://doi.org/10.1086/338209
Baumeister, R. F., Vohs, K. D., & Tice, D. M. (2007). The Strength Model of Self-Control. Current Directions in Psychological Science, 16(6), 351-355. https://doi.org/10.1111/j.1467-8721.2007.00534.x
Carver, C. S., & Scheier, M. F. (1981). The self-attention-induced feedback loop and social facilitation. Journal of Experimental Social Psychology, 17(6), 545-568. https://doi.org/10.1016/0022-1031(81)90039-1
Gillebaart, M. (2018). The ‘operational’definition of self-control. Frontiers in psychology, 9, 1231. https://doi.org/10.3389/fpsyg.2018.01231
Inzlicht, M., Schmeichel, B. J., & Macrae, C. N. (2014). Why self-control seems (but may not be) limited. Trends in cognitive sciences, 18(3), 127-133. https://doi.org/10.1016/j.tics.2013.12.009
Kroehne, U., & Goldhammer, F. (2018). How to conceptualize, represent, and analyze log data from technology-based assessments? A generic framework and an application to questionnaire items. Behaviormetrika, 45(2), 527-563. https://doi.org/10.1007/s41237-018-0063-y
Mau, G., Schramm-Klein, H., & Reisch, L. (2014). Consumer socialization, buying decisions, and consumer behaviour in children: Introduction to the special issue. Journal of Consumer Policy, 37(2), 155-160. https://doi.org/10.1007/s10603-014-9258-0
Mau, G., Schuhen, M., Steinmann, S., & Schramm-Klein, H. (2016). How children make purchase decisions: Behaviour of the cued processors. Young Consumers, 17(2), 111-126. https://doi.org/10.1108/YC-10-2015-00563
Powers, W. T. (1973). Behavior: The Control of Perception. Chicago, IL: Aldine.
Silberer, G. (2009). Verhaltensforschung am Point of Sale-Ansatzpunkte und Methodik. Universitätsverlag Göttingen.
09. Assessment, Evaluation, Testing and Measurement
Paper
Learning Collaboration in the School Context in Serbia: Student Perceptions
Dragica Pavlović Babić, Marina Videnović, Smiljana Jošić, Kristina Mojović Zdravković
University of Belgrade, Serbia
Presenting Author: Pavlović Babić, Dragica
The increasing interest in collaboration as an educational competence important for successful schooling and a productive adult professional and civic life can be seen in the expanding literature and research evidence (e.g. Rychen & Salganik, 2003; National Research Council, 2011). Collaboration is marked as one of the social and emotional skills on the 2030 education development agenda, defined by the intergovernmental Organisation for Economic Co-operation and Development (OECD, 2019).
Collaborative problem solving (CPS) is an umbrella term for a variety of pedagogical models that enable students to learn by engaging in joint activities, relying on each other, integrating individual knowledge, skills, and efforts (Lai, 2011). With appropriate support and scaffolding, CPS could have a greater positive effect on student achievement, and peer social relationships than competitive and individual learning (e.g. Gillies, 2016; Johnson & Johnson, 2002).
The focus of this study is on students experience with CPS as simetric peer interaction during the regular school classes. These perceptions and experiences represented a base for examining how CPS is applying in context of secondary schools in Serbia. The research suggests that a productive collaboration requires both cognitive skills (e.g. Campbell, 2021; Shi et al, 2021), as well as social and emotional skills (e.g. Newman, 2016; Rogat & Adams-Wiggins, 2015). That is why we paid attention how students have reported about cognitive (e.g, argumentation, consideration and evaluation of various perspectives...) and social and emotional aspects (group cohesion, tolerance, atmosphere…) of collaborative work.
Analysing student responses to semi-structured interviews showed that, with certain inconsistencies and overlaps, two models of cooperation are clearly differentiated, presented here by key features.
Model 1 is oriented towards an efficient use of resources, including time, with a dominant utilitarian goal - getting the job done. It is characterized by a strict division of responsibilities, usually mechanical. The roles are defined, including the leader who can be self-proclaimed. The product is a collection of individual works: either loosely bound or bound by one group member. Solution/product quality is judged on the basis of external indicators. Cognitive aspect of CPS includes prior knowledge seen as a key success factor. Social and emotional aspect: a strict division of roles and the leader’s assumption of responsibility often excludes democratic patterns of behaviour such as negotiation and agreement; the atmosphere in the group depends on the degree of closeness of the members, any disagreement during group work can grow into a conflict.
This model can be termed parallel or utilitarian and quasi-cooperation, as the key cooperation determinants cannot be easily identified, except for work arrangements. According to our respondents’ experiences, this model dominates.
Model 2 is oriented primarily towards product quality; sometimes learning cooperation as a competence is cited as an explicit cooperation goal. Cooperation primarily has a cognitive goal, reflected in the usage of search strategies for task solving. There is a loose division of responsibilities and roles, usually according to participant competencies and interests; deadlines are on the back burner. The product is based on group consensus. Cognitive aspect includes awareness of the importance of argumentation and discussion Social and emotional aspect: an atmosphere of mutual trust and equality between team members remove barriers and allow freedom in presenting and considering different solutions and/or ways of solving tasks. There is mutual knowledge and respect. Cognitive and social and emotional aspects are interconnected, manifesting itself as solidarity with others and connecting as a form of strengthening personal capacities.
This model can be called collaborative or constructive due to its orientation towards the joint construction of knowledge. Unfortunately, according to student experiences, it is rarely represented in school practice.
Methodology, Methods, Research Instruments or Sources UsedThe study was conducted at the end of the 2021/2022 school year and included six secondary schools in Belgrade (3 vocational and 3 general/gymnasium schools). The sample consisted of 31 second grade students (17 female), 15-17 years old. All students involved in research had a formal parental consent and their assent. Students were examined with a semi-structured interview which lasted approximately 60 minutes.
The adolescent answered the questions related to their perceptions of cooperation in everyday school work. The interview guide consists of five indicators, i.e. thematic units. The first indicator referred to the general impression of cooperation in the school context, whereupon the students were asked about the frequency and quality of peer cooperation in and outside school. The second theme was peer cooperation in the school context - what the organization of group work looks like in and outside class, and what are the advantages and disadvantages of group work concerning individual school work. The third indicator included questions related to the recognition of successful and unsuccessful peer cooperation factors, where they discussed the roles of different actors in group work and described the experiences of successful and unsuccessful group works in which they had participated. The fourth topic was cooperation as a competence, where the accent was on how competence is acquired and manifested, its importance, as well as whether and to what extent young people possess it. Finally, the fifth topic covered personal perspective, i.e. an assessment of personal competences for cooperation.
Following the coding of interview transcripts, 612 coded segments were analyzed using MaxQDA according to thematic analysis.
Conclusions, Expected Outcomes or FindingsSeveral conclusions can be drawn from these findings, with significant implications for the organization of regular classes in the Serbian educational system.
During joint work at school, important aspects of CPS (argument, sharing ideas...) are often missing. Research shows that the successful development of collaborative skills requires the support of adults (eg, Gonzalez-Howard & McNeill, 2019; Rojas-Drummond & Mercer, 2003). Our results indicate that this support is often lacking. It is necessary to think about how to organize teaching that would go in the direction of encouraging the development of these skills.
The parallel model, although more present in school practice, is a model that supports quasi-cooperation, as it only has the form of cooperative work, but lacks the features of the processes that define cooperation. Student learning of quasi-cooperation can have lasting implications for student competencies, and thus it is recommended that the system recognize this organizational form of work as not effective.
Time, restricted on 45 minutes what is the duration of school hours, could be an obstacle to organize the cooperation in the school context. We perceive time management as a particularly sensitive point in collaborative learning and/or learning through collaboration. A strategy that is often applied in these cases is to transfer a task to an extracurricular environment (such as homework), which has both good and bad sides.
Finally, the two models presented are not developmental stages in the learning of cooperation in the school context but rather, two qualitatively different approaches. In fact, practicing the first one will not enable a transition to the second, constructive model.
ReferencesRychen, D. S., & Salganik, L. H. (Eds.). (2003). Key competencies for a successful life and a well-functioning society. Hogrefe & Huber Publishers.
National Research Council. (2011). Assessing 21st Century Skills: Summary of a Workshop. National Academies Press (US). http://www.ncbi.nlm.nih.gov/books/NBK84218/
OECD. (2019). Future of Education and Skills 2030. OECD Publishing.
Lai, E. (2011). Collaboration: A Literature Review. Pearson, Princeton.
Gillies, R. M. (2016). Cooperative learning: review of research and practice. Australian Journal of Teacher Education (Online), 41(3), 39-54. https://search.informit.org/doi/10.3316/informit.977489802155242
Johnson, D., Johnson, R. (2002). Learning together and alone: Overview and meta-analysis. Asia Pacific Journal of Education, 22, 95-105. https://doi.org/10.1080/0218879020220110
Campbell, T. (2021). Examining how middle grade mathematics students seize learning opportunities through conflict in small groups. Mathematical Thinking and Learning. https://doi.org/10.1080/10986065.2021.1949529
Shi, Y. C., Shen, X.M., Wang, T., Cheng, L. & Wang, A.C. (2021). Dialogic teaching of controversial public issues in Chinese middle school. Learning Culture and Social Interaction, 30. https://doi.org/10.1016/j.lcsi.2021.100533
Newman, R. (2016). Working talk: developing a framework for the teaching of collaborative talk. Research Papers in Education, 31(1), 107–131. https://doi.org/10.1080/02671522.2016.1106698
Rogat, T. K., & Adams-Wiggins, K. R. (2015). Interrelation between regulatory and socioemotional processes within collaborative groups characterized by facilitative and directive other-regulation. Computers in Human Behavior, 52, 589-600. https://doi.org/10.1016/j.chb.2015.01.026
09. Assessment, Evaluation, Testing and Measurement
Paper
Examinee Timing Indicators, Measured on a Continuous Scale. Some Insights from E-TIMSS 2019 in Mathematics
Elena Papanastasiou1, Michalis Michaelides2
1University of Nicosia, Cyprus; 2University of Cyprus
Presenting Author: Papanastasiou, Elena
When participating in assessments, it is assumed that examinees have invested effort to perform well; otherwise, scores will not reflect their true ability and will not be valid indicators of their proficiency (Baumert & Demmrich, 2001; Wise, 2015). However, lack of motivation and effort during the test-taking process, creates a threat to the validity of test outcomes, especially with International Large-Scale Assessments (Rutkowski & Wild, 2015).
Response-time data from computerized tests have enabled researchers to study test-taking effort, e.g. by identifying respondents who respond rapidly before a certain time point. However, there is a need to move beyond the examination of rapid responses through thresholds, since the time needed to respond to a test item is dependent on various factors, including ability and test-taking behaviors. Consequently, it is argued that to be interpreted appropriately, response times should be examined in relation to examinee’s performance at the item level.
The purpose of this study is to examine two novel response-based, indicators of test-taking behaviors that utilize a combination of examinee response and process (timing) data to better understand and describe test-taking effort in online assessments. These indicators, which have been named “Unsuccessful time management” and “Successful time management” will be empirically estimated with data from the fourth-grade e-TIMSS 2019 mathematics assessment. This study further aims to examine these variables in relation to achievement benchmarks, student background characteristics such as attitudes towards mathematics, confidence in mathematics, gender, as well as overall achievement. The ultimate goal of these analyses is to try to obtain further insights on examinees who participate in online assessments through the use of their timing data.
Methodology, Methods, Research Instruments or Sources UsedThe sample utilized in the study was that of grade 4 students from the USA who had participated in e-TIMSS 2019. The sample included 10029 students, of which 49.44% were female. The average age of the students was 10.29 years of age (SD=0.43)
To calculate the indicators of the current study, the average time spent on an item was first calculated for each test item separately. At a second stage, a deviation score was calculated for each student who was administered item i, by subtracting the average sample screen time for item i from the students’ time for the same item. Based on these deviation scores, a cumulative indicator was calculated as follows:
1) For items that were omitted or were answered incorrectly in less time than average, this negative timing difference was added to the Unsuccessful Time Management indicator for the examinee. Therefore, this indicator represents the sum of the unused time that was spent on test items that were answered incorrectly indicating that most likely, the students made less than adequate effort to answer them correctly.
2) For items that were answered correctly in less time than average, this negative timing difference was added to the Successful Time Management indicator for the examinee. This indicator represents the sum of the unused time that was spent on correct answers, indicating that most likely the students were either already proficient on the specific content and thus did not need additional time to correctly respond to those items, or that the correct answer was a consequence of lucky guess.
Overall, 86.459% of the participants utilized less time than average on at least one item of their incorrect responses. This resulted in an average of 320.881 (sd=166.792) seconds of unused time for those examinees. Of the 48.489% of the participants who utilized less time than average on at least one of their correct responses, had an average of 20.617 seconds (sd=17.576) of unused time. The correlation between these two indicators was 0.29 (se=0.01)
Conclusions, Expected Outcomes or FindingsThe results of this study showed that when examining these indicators by benchmark level, as the benchmark levels increase, the successful time management indicator increased, while the unsuccessful time management indicator decreased. Also, students who spent less time in incorrect answers tended to be in the lower benchmarks. The correlation between the Successful Time Management indicator and achievement equaled r=0.25 (se=0.01), while the correlation between the Unsuccessful Time Management indicator and achievement equaled r=-0.08 (se=0.02). So, the students with higher levels of achievement tended to have more unused time on their correct answers (thus, most likely being an indicator of mastery of the test content), and had less unused time for their incorrect answers. This indicated that they generally struggled more with such items; however, this relationship was very small.
Further analyses found that 89.50% of the students who reached all test items, had the most amount of unused time. Most likely, this occurred while trying to ensure that they had time to complete the test. These were also the students who had the highest average achievement (M=537.75, SD=84.80). The students who ran out of time were the ones who had the least amount of unused time. These are most likely students who spent more time than average on most items, which resulted in their running out of time in the end. Finally, the 5.36% of the students that stopped responding, were most likely the students who made the least amount of effort and had the lowest average achievement (M=482.50, SD=81.59) which further verifies their low effort on the test.
Overall, the results of this study revealed that both indicators might provide additional insights related to examinee test-taking effort and characteristics, when conditioned on the accuracy of their responses. However, more research is needed to understand these indicators more comprehensively.
ReferencesBaumert, J., & Demmrich, A. (2001). Test motivation in the assessment of student skills: The effects of incentives on motivation and performance. European Journal of Psychology of Education, 16(3), 441-462. https://doi.org/10.1007/BF03173192
Rutkowski, D., & Wild, J. (2015). Stakes matter: Student motivation and the validity of student assessments for teacher evaluation. Educational Assessment, 20(3), 165-179. https://doi.org/10.1080/10627197.2015.1059273
Wise, S. L. (2015). Effort analysis: Individual score validation of achievement test data. Applied Measurement in Education, 28(3), 237-252. https://doi.org/10.1080/08957347.2015.1042155
09. Assessment, Evaluation, Testing and Measurement
Paper
Digital Media Use and Sleep in Young Children: Insights from Advances in Long-Term ECG Monitoring of Toddlers
Marina Eglmaier1, Sigrid Hackl-Wimmer1, Manuela Paechter1, Helmut Karl Lackner2, Ilona Papousek1, Lars Eichen1
1University of Graz, Austria; 2Medical University of Graz
Presenting Author: Eglmaier, Marina
The availability of digital media devices such as smartphones, tablets, notebooks, etc. in households has become so commonplace today that we hardly think about our media use and its consequences. This also seems to be true for households with children. Even very young children come in contact with and use these devices from an early age on. For example, a study in the UK reported that children started using touchscreen media (smartphones, tablets) as early as six months of age (Cheung et al., 2017). As for the duration of media use, an Austrian study of parents with children up to the age of six years found that one-third of the children use digital media every day and around fifty percent use them several times a week (Institut für empirische Sozialforschung [IFES], 2020). The numbers are similar across Europe (e.g., Germany see Kieninger et al., 2020; UK see Bedford et al., 2016; France see Cristia & Seidl, 2015; Italy see Chindamo et al., 2019).
Parents face various educational challenges and must deliberate how to manage and regulate their children’s media use. For the investigation of parents’ educational strategies and behaviors, “parental mediation theory” has proven to be a valuable theoretical framework. Within this framework, the present study focused on children’s sleep and parents’ mediation of media use. The prevalence of digital media presents parents with several challenges, including the duration and frequency of media use but also concerns about possible harmful effects of media use itself. In this regard, parental mediation theory describes strategies parents use to minimize potentially harmful consequences of media use (Clark, 2011; Valkenburg et al., 1999).
One important area of research concerns the effects of media use on children’s development and health. In this context, sleep is an important variable. During the first years of life, sufficient and restful sleep is of particular importance, because sleep is essential for developmental processes (El-Sheik & Sadeh, 2015) such as neuronal and cognitive development processes (Ednick et al., 2009). Research suggests harmful effects of media use on children’s sleep (e.g., Hackl-Wimmer et al., 2021). Ensuring that children get sufficient and restful sleep is an important educational task for parents. Toddlers’ sleep may be influenced by a variety of factors, including digital media use (Hackl-Wimmer et al., 2021). There are diverse methods for quantifying sleep quantity and quality. However, research on children’s sleep suffers from methodological problems. Studies on young children’s sleep and media use mainly employ subjective data such as parent questionnaires to assess children’s sleep (e.g., Chindamo et al., 2019).
An approach that is often used in medicine is polysomnography (PSG). PSG comprises the recording of several physiological functions (e.g., heart rate and brain waves) in a sleep laboratory for clinical purposes. However, the methodology is not suitable for recording children’s sleep in daily situations at home. Therefore, we used and further developed a more practical approach, namely ECG (electrocardiogram) recordings with small portable devices. ECG is the recording of the electrical activity of the heart. ECG data allows the measurement of heart rate (HR) and the calculation of several parameters of heart rate variability (the variation in time between consecutive heartbeats). Due to technological progress, ECG data can be recorded using portable devices over a period of 24 hours or more. These devices are also suitable for studies with young children and allow recording of HR during sleep at home in the child’s familiar environment.
The aim of this study is to examine whether toddlers’ use of smartphones and audio media is related to their sleep quality (quantified as HR during restless sleep phases).
Methodology, Methods, Research Instruments or Sources UsedIn the present study, two methodological approaches and their intertwining are used: a questionnaire on parental mediation behavior, children’s media use, and other variables plus long-term ECG monitoring.
The questionnaire included several types of media (e.g., smartphone and audio media) for which the duration of use on weekdays and the weekend was asked. Furthermore, parents were asked what their objectives were for their children’s smartphone and audio media use and for which activities their children used these devices.
To investigate toddlers’ sleep quality, long-term ECG monitoring was performed for approximately 30 hours. Data collection was performed as part of a field study at crèches in Austria and started in the morning at the crèches. The ECG device used is equipped with an integrated 3D acceleration sensor that provides information about body position and body movement. For the duration of the ECG measurement, parents and daycare educators were asked to keep an activity log to record the children’s activities with begin and end times (e.g., sleep during the day and night and other activities such as mealtimes and media use). Processing of the ECG Data and the quantification of sleep quality involved several steps. The two main steps comprised the following: First, ECG data, acceleration sensor data and the activity log recordings were used to determine restful and restless sleep phases. Major determinants for restful sleep are a lying position, little body movement and a calm, steady respiration pattern. Phases of restful sleep had to last at least ten consecutive minutes to be classified as restful sleep for further analysis. Otherwise, the sleep phases were classified as restless sleep. In the second step, heart rate (HR) was calculated for restless sleep. The statistical analysis comprised partial correlations to investigate potential relationships between media use and HR. The analysis included the children’s age (in months) and were calculated separately for smartphone and audio media use on weekdays, the weekend and the average weekly media use. Additionally, descriptive statistics are reported on the activities and objectives for the use of these devices.
Conclusions, Expected Outcomes or FindingsThe results showed that smartphone use is associated with poorer night’s sleep (i.e., higher HR during restless sleep). However, audio media use is associated with more favorable sleep (i.e., lower HR during restless sleep), indicating that the investigation of media effects benefits from differentiating between media types.
The joint consideration of physiological data and parents’ educational behavior gives more insights into possible causes. Regarding the objectives of media use, parents most often reported that smartphones and audio media are used by their children for entertainment purposes or out of boredom. While parents most often reported that their children used the smartphone for activities such as watching movies, listening to music, and playing educational games, audio media were mainly used for listening to music and books. A possible explanation for the present results could be that smartphone use is related to sustained arousal due to its interactive component and the emitted blue light. On the other hand, assuming that calming content is played, using audio media to listen to music and books could help children to relax and unwind.
However, the possible selectivity of the sample must be taken into account. Media use was lower compared to other studies (e.g., Cheung et al., 2017), as some toddlers did not use smartphones or audio media at all.
In conclusion, not every type of media is detrimental to children’s sleep, so further research on media content is needed. ECG monitoring during the use of different types of media content allows the detection of psychophysiological processes that young children are unable to reflect and report on. Implications for education and development comprise the selection of media content appropriate to the situation, meaning arousing or exciting content for playtime, for example, and calming content for relaxing situations such as before bedtime.
ReferencesBedford, R., Saez de Urabain, I.R., Cheung, C.H.M., Karmiloff-Smith, A., & Smith, T. J. (2016). Toddlers’ fine motor milestone achievement is associated with early touchscreen scrolling. Frontiers in Psychology, 7, 1108.
Cheung, C.H.M., Bedford, R., Saez De Urabain, I.R., Karmiloff-Smith, A., & Smith, T.J. (2017). Daily touchscreen use in infants and toddlers is associated with reduced sleep and delayed sleep onset. Scientific Reports, 7, 46104.
Chindamo, S., Buja, A., DeBattisti, E., Terraneo, A., Marini, E., Gomez Perez, L.J., Marconi, L., Baldo, V., Chiamenti, G., Doria, M., Ceschin, F., Malorgio, E., Tommasi, M., Sperotto, M., Buzzetti, R., & Gallimberti, L. (2019). Sleep and new media usage in toddlers. European Journal of Pediatrics, 178(4), 483–490.
Clark, L.S. (2011). Parental mediation theory for the digital age. Communication Theory, 21, 323–343.
Cristia, A., & Seidl, A. (2015). Parental reports on touch screen use in early childhood. PloS ONE, 10(6), e0128338.
Ednick, M., Cohen, A.P., McPhail, G.L., Beebe, D., Simakajornboon, N., & Amin, R.S. (2009). A review of the effects of sleep during the first year of life on cognitive, psychomotor, and temperament development. Sleep, 32(11), 1449–1458.
El‐Sheikh, M., & Sadeh, A. (2015). I. Sleep and development: Introduction to the monograph. Monographs of the Society for Research in Child Development, 80(1), 1–14.
Hackl-Wimmer, S., Eglmaier, M.T.W., Eichen, L., Rettenbacher, K., Macher, D., Walter-Laager, C., Helmut K.L., Papousek, I., & Paechter, M. (2021). Effects of touchscreen media use on toddlers’ sleep: Insights from longtime ECG monitoring. Sensors, 21(22), 7515.
Institut für empirische Sozialforschung. (2020). Die Allerjüngsten (0-6 J.) & digitale Medien [The very young (0–6 years) & digital media]. https://www.saferinternet.at/fileadmin/redakteure/Projekt-Seiten/Safer_Internet_Day/Safer_Internet_Day_2020/Praesentation_PK_Safer_Internet_Day_2020.pdf
Kieninger, J., Feierabend, S., Ratgeb, T., Kheredmand, H., & Glöckler, S. (2020): miniKIM-Studie 2020. Kleinkinder und Medien: Basisuntersuchung zum Medienumgang 2- bis 5-Jähriger in Deutschland. www.mpfs.de/fileadmin/files/Studien/miniKIM/2020/lfk_miniKIM_2020_211020_WEB_barrierefrei.pdf
Valkenburg, P.M., Krcmar, M., Peeters, A.L., & Marseille, N.M. (1999). Developing a scale to assess three styles of television mediation: “Instructive mediation,” “restrictive mediation,” and “social coviewing”. Journal of Broadcasting & Electronic Media, 43(1), 52–66.
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