Research Article 2

Structure and Strategies in Children’s Educational Television: The Roles of

Program Type and Learning Strategies in Children’s Learning

Deborah L. Linebarger and Jessica Taylor PiotrowskiUniversity of Pennsylvania

Educational TV has been consistently linked to children’s learning. In this research, educational TV character-istics were identified, coded, and tested for their influence on children’s program-specific comprehension andvocabulary outcomes. Study 1 details a content analysis of TV features including a program’s macrostructure(i.e., narrative or expository) and learning strategies embedded in the macrostructure that support learning inprint-based contexts. In Study 2, regression analyses were used to predict outcomes involving 71 second andthird graders (average age = 7.63 years). Strategies were categorized as organizing, rehearsing, elaborating, oraffective in function. Outcomes were uniformly higher for narrative macrostructures. Strategies used in narra-tives predicted relatively homogenous relations across outcomes, whereas strategies in expositories predictedquite heterogeneous relations across outcomes.

Since the introduction of Sesame Street in 1969, pro-ducers, educators, and researchers have workedtogether and in parallel to create and evaluate theimpacts of educational TV. When programs are cre-ated using entertaining formats guided by develop-mental theory, researchers document increases onvarious outcomes including school readiness (e.g.,Wright et al., 2001), problem solving (e.g., Crawley,Anderson, Wilder, Williams, & Santomero, 1999),and literacy (e.g., Linebarger, Kosanic, Greenwood,& Doku, 2004). There is growing evidence thatembedding content with certain learning strategiessupports children’s processing of that content.Showing a word that breaks into individual soundsand then reassembles into the word helps earlyreaders acquire phonemic awareness (e.g., mm, aa,pp when blended together forms map; Linebargeret al., 2004; Uchikoshi, 2005) while pairing visual

referents with verbal labels or using intonationalcues to emphasize content helped children learnmore (Fisch, 2004).

Learning involves a relatively permanent changein behavior or knowledge. The most straightforwardtheories of learning from TV are content based andinvolve the acquisition, encoding, storage, andretrieval of information. These include behaviorchange (e.g., social cognitive theory; Bandura, 2001)and information-processing theories (capacitymodel; Fisch, 2004) that, in their broadest forms,predict that knowledge results from cognitivelyprocessing televised content. When children canprocess and comprehend visual and verbal TV sym-bols, they will be able to organize, store, and relatethis information to prior knowledge. Acquiredknowledge and behavior are then used to influencea child’s actions.

Focusing attention on particular televised stimuliis dependent on an interaction among interest, abil-ity, and characteristics inherent in stimuli. Becom-ing sufficiently aroused and oriented to a stimuluswhile blocking out other, irrelevant stimuli (Herr-man, Yoder, Gruneberg, & Payne, 2006) is neces-sary for learning. Children direct maximal attentionto stimuli perceived to be moderately novel and ofintermediate complexity (Rice, Huston, & Wright,1982; Roller, 1990). Declining attention occurs whenthe material is overly familiar and easy to process,or, conversely, too novel and complex. One way to

This project was supported by two U.S. Department of Educa-tion (DOE) grants (H029D60040; H327A990082) and a third U.S.DOE cooperative agreement (U295A050003). Please note, thesecontents do not necessarily represent the policy of the U.S. DOEand you should not assume endorsement by the federal govern-ment. Portions of the research were presented at the annualmeeting of the Office of Special Education Projects, U.S. DOE,Washington, DC, June 2002, and at the biennial meeting of theSociety for Research in Child Development, Tampa, FL, April2003. Special thanks to the staff and participants involved in thisproject including Dr. John C. Wright of the University of Texasat Austin who was a coinvestigator on this project until his deathin 2001. We are indebted to his many contributions. His guid-ance and mentoring will be missed by all those who knew himor benefited from his scholarly brilliance.

Correspondence concerning this article should be addressed toDeborah L. Linebarger, Annenberg School for Communication,University of Pennsylvania, 3620 Walnut Street, Philadelphia, PA19104. Electronic mail may be sent to [email protected].

Child Development, September/October 2010, Volume 81, Number 5, Pages 1582–1597

� 2010 The Authors

Child Development � 2010 Society for Research in Child Development, Inc.

All rights reserved. 0009-3920/2010/8105-0019

boost attention and support content retention is toinsert learning strategies that support information-processing efforts. There is considerable variabilityin the degree of instructional support provided byprograms, ranging from those that rely heavily onviewers to generate and apply their own strategiesto those that use strategies to explicitly structureviewers’ processing in an effort to stimulate learn-ing (Smith & Ragan, 2005).

Recently, educational media began creating con-tent to support the development of early literacyskills. The purpose of this study was to evaluate aset of learning strategies hypothesized to supportchildren’s literacy abilities in both narrative andexpository programs. To operationalize and evalu-ate these learning strategies, two studies were con-ducted. In Study 1, various literacy-promotinglearning strategies were culled from print and otherliteracy-related research and then coded for theirpresence in children’s educational TV. All pro-grams used one of two program types or macro-structures: narrative or expository. In Study 2,learning strategies and macrostructures were usedto predict program-specific comprehension andvocabulary.

Study 1

A number of learning strategies that support print-based literacy were identified from several sources(National Institute of Child Health and HumanDevelopment, 2000; Snow, Burns, & Griffin, 1998).Because research on the effects of TV on youngchildren has established the crucial role that contentplays, because programs with well-designed curric-ula produce stronger academic and social effects(e.g., Anderson, Huston, Schmitt, Linebarger, &Wright, 2001), and because strategies typically workacross different media, it was appropriate to iden-tify learning strategies used in other contexts thatmight also be found in educational TV. Via an itera-tive process of review and coding, three key sets ofstrategies were identified: affective strategies (i.e.,literacy environment), comprehension strategies,and vocabulary strategies. Each is reviewed next.

Literacy Environment

Programs may explicitly or implicitly create sup-portive literacy environments by depicting charac-ters who use print or make positive commentsabout literacy as well as by displaying printonscreen. Making a decision to invest attention and

cognitive resources to televised content is depen-dent on a child’s personal interest, the stimuli char-acteristics, and the interaction between the two.Personal interest develops when the child’s percep-tion of the rewards for engaging in the modeled setof behaviors is strong (Schiefele, 1998). According toBandura’s (2001) social cognitive theory, childrenobserve and imitate persons found in their every-day worlds, including characters on TV, particu-larly when those characters are interesting andpersonally relevant. Stimuli characteristics also gen-erate interest and attention when they help childrenbelieve they could engage in and successfully repli-cate onscreen behavior or that literacy is valuedand respected. When questioned about their com-petence in learning from TV and print, the compe-tence scores of poor at-risk second graders wererated more highly for learning from TV (Linebar-ger, 2001). Motivation levels increased wheninstruction was based on popular TV programs(Szabo & Lamielle-Landy, 1981). Greater motivationresults in deeper processing of content (Renninger,1992) that, for TV, was found to transfer to higherfourth-grade reading achievement (Szabo & Lami-elle-Landy, 1981). Two longitudinal studies involv-ing preschoolers also provide support: Watchingeducational TV as a preschooler predicted betterschool readiness skills at school entry (Wright et al.,2001) and more leisure book reading, greater aca-demic self-concept, and higher English grade pointaverages when a teen (Anderson et al., 2001).

Onscreen print should contribute to literacyskills when that print is a natural part of the plot(e.g., characters write notes or grocery lists; Fisch,2004; Linebarger et al., 2004). Viewers are givenopportunities to practice reading onscreen printwith visual (e.g., print grows and shrinks) and ver-bal supports (e.g., intonation cues). Receiving stim-uli in multiple modalities strengthens theconnections among these stimuli, resulting in effi-cient encoding and later retrieval (Sadoski & Paivio,2001). Incorporating print as an extension of charac-ter or program events links it to the main theme,making it more meaningful and easier to remember(Fisch, 2004).

Comprehension Strategies

Constructing meaning from content requiresprior knowledge about that content and explicitlinks between that knowledge and new content.Strategies that support comprehension includeactivation of prior knowledge (via advance orga-nizers or connections to real life) and repetition

Television Features and Literacy 1583

and review of the content after it has been pre-sented (Michel & Roebers, 2008). Comprehensionis also supported when new information is lim-ited (Reinking & Schreiner, 1985) or when readersare directed to make predictions about upcomingcontent (Taylor, Peterson, Pearson, & Rodriguez,2002). Using comprehension strategies should helpchildren remember more of an episode as well aslearn the underlying educational content in thatepisode.

Vocabulary Strategies

Children are able to learn specific words fromTV as well as transfer that knowledge to moregeneralized vocabulary gains (Rice & Woodsmall,1988; Wright et al., 2001); however, these associa-tions hold only for educational TV (e.g., Patterson,2002). Naigles and Mayeux (2001) concluded thatwhen designed appropriately using strategies thatsupport word learning in print-based contexts,educational TV helps vocabulary acquisition. TVaffords a unique opportunity to present wordsand concepts using a combination of images, ver-bal description, and sound effects. When support-ing text is simple, more challenging content canbe inserted. The simple text requires little process-ing and, as such, directs attention to the centralcontent and away from incidental content. Therate of aural narration also affects what and howmuch is comprehended. Pairing static text withnarration (e.g., books on tape) increased readingvocabulary and definition knowledge when narra-tion rates were similar to or just above the child’sreading rate (McMahon, 1983). Televised andaudio-taped book narration rates average 141words per minute (wpm), children’s educationalTV averages 124 wpm, and cartoons average125 wpm (Jensema, McCann, & Ramsey, 1996).Conversation rates are between 150 and 175 wpm(Taylor, 1964). While first to third graders are ableto comprehend content at these speeds, it beginsto decline as speeds reach and surpass 175 wpm(Barron, 2004). Lower rates are required whenchildren have reading difficulties or when contentis especially complex (e.g., science content is morechallenging than prosocially themed stories atfaster narration rates; Barron, 2004). While thesestudies do not exactly mirror the process of view-ing and comprehending TV, recent research findsthat similar skills underlie processing of differentmedia. Comprehension of televised stories atage 6 predicted reading comprehension at age 8(Kendeou et al., 2005).

Program Type: Narrative Versus ExpositoryMacrostructures

In addition to learning strategies, the broadermacrostructure used to deliver content affectslearning. While different narrative macrostructuresexist (e.g., traditional story book, story within astory; Linebarger & Piotrowski, 2009), the founda-tional elements used in most narratives are similar(e.g., setting, character, goals, resolution). In con-trast, expository macrostructures present informa-tion in varied ways depending on the purpose ofthe information (e.g., compare and contrast, listingand sequencing, cause and effect; Duke & Kays,1998). Embedded within both macrostructures arelearning strategies that support processing byreducing cognitive load and making content acces-sible and salient. It is unclear whether these strate-gies function similarly across macrostructures orwhether their influence is dependent on, shapedby, or interacting with the macrostructure. It islikely that these strategies function in both ways.

The skills used to interpret content and thosethat develop from repeated exposure to content dif-fer by the macrostructure used. Reading expositorybooks contributes to conceptual knowledge andvocabulary development, stronger comparative andcontrastive abilities, and the use of classificatorylanguage structures (Duke & Kays, 1998). In con-trast, exposure to narrative stories supportslanguage and literacy skills, story knowledge, andlater school success (e.g., Nord, Lennon, Liu, &Chandler, 1999; Scarborough & Dobrich, 1994).

Television programs also use these two macro-structures although little research has specificallyfocused on the effects of these macrostructures.Narratives contribute to language and literacy skillacquisition (Kendeou et al., 2005; Linebarger &Piotrowski, 2009; Linebarger & Walker, 2005).Watching narratives over an extended periodhelped preschoolers augment story knowledge andprint-based narrative skills compared with childrenwho viewed an expository or no program (Linebar-ger & Piotrowski, 2009). Story knowledge and nar-rative abilities develop from the ability to identifythe meaning of a particular story. When a programuses a narrative macrostructure, children should bebetter able to recognize story parts (e.g., goals, char-acters, setting) and, more important, the deepereducational content (Fisch, 2004).

The utility of expositories, comparisons betweenthese programs and narratives, and comparisonsbetween these programs and comparable expositoryprinted texts, have not been directly evaluated.

1584 Linebarger and Piotrowski

There is, however, sufficient evidence that childrenlearn content from expositories and are able totransfer that content to more generalized knowledgegains (e.g., Fisch, 2004). Questions remain aboutwhat content is best learned through which macro-structure and whether particular learning strategiessupport or interfere with learning that content.

Method

Sample

Six 30-min programs previously or currently air-ing on the Public Broadcasting Service (PBS) chan-nel were selected for inclusion in this study.Curriculum documents and promotional materialsdeveloped by each of the programs indicated thatthese programs were designed for children in thetarget age group (i.e., 7- to 9-year-olds). PBS pro-grams were selected because (a) PBS is widely avail-able and free, (b) not all children in the study hadaccess to cable (e.g., for programs on Nickelodeonor Disney), and (c) there were no commercials onPBS. In addition to airing on PBS and targeting sec-ond- and third-grade children, an equal number ofnarrative and expository macrostructures wereincluded. Narratives used a set of story events con-taining dialogue, characters, setting, goals, and out-comes to deliver prosocial or academic content.Expositories used multiple vignettes and programhosts to educate, inform, or describe a particulartopic. As the goal of this research was to make moregeneral statements about macrostructures andembedded learning strategies (vs. claims about par-ticular programs), selecting six different programsequally representing both macrostructures providedstronger tests of our hypotheses. Arthur & Friends(ART), Magic School Bus (MSB), and Wishbone(WB) were selected as narrative exemplars. ReadingRainbow (RR), Zoom (ZM), and Kratts’ Creatures (KC)were selected as expository exemplars.

Coding Framework

Literacy environment. To evaluate whether theliteracy environment depicted in an episode wasconducive to learning, the following were coded:character’s use of print (i.e., reading books, writing,using the computer; Print Use), character’s positivestatements about print (i.e., frequency; Positive State-ments), and onscreen print (i.e., frequency that printonscreen lasted at least one second; Onscreen Print).

Comprehension strategies. Four comprehensionstrategies were coded. An episode’s number of

main segments was counted (i.e., segments intro-ducing new information related to an overall theme;Main Segments). Pre- and postmain segments werecoded as Prior Knowledge or Summary, respec-tively. Prior Knowledge was the duration in sec-onds that characters or a narrator spent directingviewers to access their own background knowledge.Summary was the duration in seconds when theinformation in a Main Segment was repeatedexactly or paraphrased after its initial presentation.Characters’ or narrator’s use of questions and pre-dictions related to upcoming content were scored asQuestions ⁄ Predictions (i.e., frequency).

Vocabulary strategies. A variety of componentsrelated to vocabulary were coded including numberof times target words repeated (i.e., Target Words),number of words per sentence (i.e., SentenceLength), the percentage of Dolch words used (i.e.,Dolch Words; Dolch words represent 220 high-frequency words that comprise 50%–70% of kinder-garten through third-grade primers; May, 1998),and the pace or rate of the aural narration (i.e.,Narration Rate).

Reliability

Krippendorff’s alpha (Hayes & Krippendorff,2007) was calculated to examine intercoder reliabil-ity for frequency counts and durations. Frequencycounts were tallied for Print Use, Positive State-ments, Onscreen Print, and Questions ⁄ Predictions,while durations were computed for Prior Knowl-edge and Summary. All codes demonstratedstrong intercoder reliability: Print Use = .99, Posi-tive Statements = .96, Onscreen Print = .99, PriorKnowledge = .97, Summary = .98, and Questions ⁄Predictions = .99.

The number of Main Segments was identifiedtogether with each coder prior to coding the othervariables. Vocabulary codes were calculated usingConcordance software (Watt, 2007). After enteringepisode scripts, the software counted the total num-ber and frequency of all words (including TargetWords and Dolch Words) and the average wordsper sentence (i.e., Sentence Length). Narration Ratewas calculated by dividing the total words spokenby the episode air-time. Table 1 provides descrip-tive data, and Table 2 provides correlations amongall variables.

Analytical Approach

To facilitate comparisons across codes and pro-grams and balance the contribution of each strategy


to the overall models, all coded variables werez-score transformed. Six (programs) by N (categoryof coded variables) repeated measures analyses ofvariance (ANOVAs) with repeated measures on thecategories (e.g., Literacy Environment, N = 3: PrintUse, Positive Statements, Onscreen Print) werecomputed to examine code-related program differ-ences. To evaluate whether macrostructures differ-entiated strategy use, ANOVAs comparing eachtype were computed. In these analyses, z scoreswere used to generate the F tests reported inTable 1. To ease interpretation, the original meansare presented in Table 1. Because the sample sizewas low (i.e., N = 6) and to estimate practical sig-nificance, effect sizes are also reported in Table 1.

All reported results are significant at alpha levelp < .05 or better.

Results

Variability in Strategies Across Programs

Literacy environment. To test for differences in thedepiction of the literacy environment across pro-grams, a 6 · 3 (Programs · Literacy Environment)repeated measures ANOVA with repeated measureson the last factor was conducted. The literacy envi-ronment was marginally variable across programs(p < .10). RR contained the densest literacy environ-ment followed by ART, ZM, KC, WB, and MSB.

Table 1

Means and Analysis of Variance (ANOVA) Results by Program (P) and Macrostructure (MS)

Category codes

Program means

ANOVA, F (partial g2)

Narrative Expository

ART MSB WB RR ZM KC

Literacy environment (LE) by program (P) P LE P · LE

PU 2.0 7.5 22.0 1.5 6.0 14.0 3.67� (0.753) 0 (0) 1.16 (0.492)

PS 4.5 0.5 5.0 1.5 1.5 1.5

OP 18.0 25.5 21.0 17.5 54.5 12.5

Literacy environment (LE) by macrostructure (MS) MS LE MS · LE

PU 12.67 14.00 0.95 (0.087) 0 (0) 0.09 (0.019)

PS 2.17 2.67

OP 20.33 29.33

Comprehension strategies (CS) by program (P) P CS P · CS

MS 5.0 8.0 5.5 6.0 9.0 9.5 14.52** (0.924) 0 (0) 1.68 (0.584)

PK 14.0 399 194.0 409.5 129.5 353.0

SM 111.5 417.5 156.5 1143.0 98.0 1131.0

QP 66.5 85.5 22.0 63.5 46.5 33.0

Comprehension strategies (CS) by macrostructure (MS) MS CS MS · CS

MS 6.33 8.00 1.45 (0.126) 0 (0) 9.03** (0.772)

PK 316.17 225.50

SM 557.33 461.83

QP 71.83 33.83

Vocabulary strategies (VS) by program (P) P VS P · VS

TW 13.0 33.0 25.0 23.5 28.5 27.0 3.64� (0.752) 0 (0) 6.47*** (0.844)

NR 10.7 9.6 84.6 99.1 113.4 109.1

SL 5.5 4.9 9.2 6.1 7.3 8.9

%D 47.2 43.3 5.5 47.5 54.8 46.7

Vocabulary strategies (VS) by macrostructure (MS) MS VS MS · VS

TW 23.17 26.83 15.16** (0.602) 0 (0) 1.16 (0.303)

NR 96.82 102.39

SL 5.50 8.45

%D 46.04 50.48

Note. Code ⁄ range: PU = Print Use (2–32 times); PS = Positive Statements (0–6 statements); OP = Onscreen Print (5–71 instances);MS = Main Segments (5–12 segments); PK = Prior Knowledge (100–627 s); SM = Summary (79–1273 s); QP = Questions andPredictions (9–96 times); TW = Target Words (11–36 repetitions); NR = Narration Rate (81.9–119.0 words spoken per minute);SL = Sentence Length (4.5–9.8 words per sentence); %D = Percent Dolch Words (42.4%–54.0%).�p < .10. **p < .01. ***p < .001.


Comprehension strategies. To evaluate whethercomprehension strategies differed across programs,a 6 · 4 (Programs · Comprehension Strategies)repeated measures ANOVA with repeated mea-sures on the last factor was computed. Comprehen-sion strategies significantly varied across programswhile the distribution and quantity of each wassimilar within programs. MSB contained the mostcomprehension strategies followed by WB, KC, ZM,ART, and RR.

Vocabulary strategies. To examine variation inthe use of vocabulary strategies, a 6 · 4 (Pro-grams · Vocabulary Strategies) repeated measuresANOVA with repeated measures on the last fac-tor was computed. Vocabulary strategies variedmarginally across programs. ZM contained themost, followed by KC, RR, WB, MSB, and ART.The distribution of strategies within programsalso differed. The density of all four strategieswas higher than average in ZM and lower thanaverage in WB. Both KC and RR contained morestrategies than average across three of the fourcodes (i.e., KC contained a lower than averagepercentage of Dolch Words while RR contained aslower than average Narration Rate). The reversewas true for ART and MSB; both contained fewerthan average strategies for three of the four codes.ART’s Narration Rate was faster than averagewhile MSB repeated Target Words more oftenthan average.

Macrostructure Differences

Literacy environment. To test if the literacyenvironment varied by macrostructure, a 2 · 3(Macrostructure · Literacy Environment) repeatedmeasures ANOVA with repeated measures on the

last factor was computed. The literacy environmentdid not vary by macrostructure.

Comprehension strategies. The macrostructurewas hypothesized to differentiate comprehensionstrategies such that narratives would use moreeffective comprehension strategies. This hypothesiswas tested by computing a 2 · 4 (Macrostructure ·Comprehension Strategies) repeated measuresANOVA with repeated measures on the last factor.This hypothesis was supported. The significanttwo-way (Macrostructure · Comprehension Strate-gies) interaction indicated that narratives usedmore supportive comprehension strategies andfewer comprehension obstacles compared withexpositories (i.e., longer Prior Knowledge and Sum-mary segments, more Questions ⁄ Predictions, fewerMain Segments than expositories).

Vocabulary strategies. Macrostructure was hypothe-sized to differentiate the use of vocabulary strategies suchthat expositories would use more effective vocabu-lary strategies. To test this hypothesis, a 2 · 4(Macrostructure · Vocabulary Strategies) repeatedmeasures ANOVA with repeated measures on thelast factor was computed. This hypothesis receivedpartial support. The significant two-way (Macro-structure · Vocabulary Strategies) interactionindicated that expositories contained more of allstrategies, supportive (i.e., more repetitions ofTarget Words, greater percentage of Dolch Words)and unsupportive (i.e., longer Sentence Length,faster Narration Rates) when compared withnarratives.

Discussion

Study 1 confirmed modest to high levels of vari-ability in learning strategies across and within

Table 2

Correlations Among Literacy Environment, Comprehension Strategies, and Vocabulary Strategies

Variables 1 2 3 4 5 6 7 8 9 10 11

1. Print use —

2. Positive statements .40*** —

3. Onscreen print ).12*** ).40*** —

4. Main segments ).39*** ).58*** .33*** —

5. Prior knowledge ).24*** ).23*** ).43*** ).16*** —

6. Summary ).09* ).43*** ).41*** .16*** .55*** —

7. Questions ⁄ predictions ).21*** ).42*** .09* .01 .12*** ).06 —

8. Target words ).24*** ).68*** .47*** .64*** .00 .22*** .03 —

9. Narration rate ).40*** ).31*** .36*** .67*** ).28*** .17*** ).04 .13*** —

10. Sentence length .12** .31*** ).10** .26*** ).18*** .09** ).92*** .11** .17*** —

11. Dolch words ).02 .25*** .57*** .19*** ).41*** ).42*** ).45*** ).06 .40*** .47*** —

*p < .05. **p < .01. ***p < .001.


programs. Macrostructures provided a frameworkto which microlevel learning strategies wereaffixed. Significant variability by macrostructure forcomprehension and vocabulary strategies wasfound while the literacy environment was depictedsimilarly for both.

Narratives contained fewer main segments, eas-ier program text, and more time spent activatingprior knowledge and summarizing program con-tent. These strategies should reduce the cognitiveresources needed to make sense of content byexplicitly instructing viewers to access relevantbackground knowledge and by offering time toreflect on new content during summaries. Chil-dren, by virtue of their considerable narrativeexperience (e.g., 92% of the top 25 children’s pro-grams in 2003 used narratives, Nielsen MediaResearch, 2003; 90% of books in kindergartenthrough third grade use narrative formats, Stein &Trabasso, 1982), should be able to devote fewerresources to processing narrative macrostructures,freeing up cognitive capacity to generate inferences.

Conversely, expositories are organized to trans-mit information and, as a consequence, containedmore unique content ideas and greater structuralcomplexity than narratives. As a result, exposito-ries tend to demand greater organizational andprocessing skills than is typically required for nar-ratives (Best, Floyd, & McNamara, 2008). Both sup-portive and unsupportive vocabulary learningstrategies were highest in expositories while com-prehension strategies were used less frequentlythan narratives. It is unclear whether the support-ive vocabulary strategies (i.e., target words, Dolchwords) are more powerful than the unsupportivestrategies (i.e., longer sentences, faster narrationrates), leading to higher literacy scores or whethervocabulary complexity nullifies the supportivevocabulary strategies, leading to lower literacyscores. In contrast, the lack of explicit comprehen-sion supports makes it likely that viewers willencounter difficulty linking their own knowledge(which, for at-risk children, is often limited) to thenew information, resulting in diminished programcomprehension.

Study 2

Research and theory assert that children’s vocabu-lary knowledge and comprehension abilities will behigher for programs that depict a more positive lit-eracy environment, that use more comprehensionstrategies, and that use more vocabulary strategies.

Understanding program content results from achild’s ability to recognize the macrostructure usedto present that content as well as the microlevelstrategies that define the organization of andelements within a particular media text. The micro-level strategies refer to how ideas are arranged andconnected to one another and how visual or verbalcues signal key content. This is successfully accom-plished when children are familiar with the pur-poses behind a particular macrostructure and, morespecifically, with the strategies that act as cues forkey information. Based on Study 1, it is hypothe-sized that each macrostructure delivers contentusing distinct microlevel strategies that are tied tothat particular macrostructure type. Narratives usecoherent and sequential text structures to presentcontent. In contrast, expositories use text structuresthat pattern information according to the purposeof the expository text (e.g., sequential, cause andeffect, compare and contrast). Because childrenhave little experience with expository texts, it islikely that they will experience difficulty in learningfrom these programs.

Macrostructures organize content by pairing itwith specific learning strategies to convey informa-tion. First, the effects of the two macrostructures onchildren’s vocabulary and comprehension scoreswere evaluated for significant differences. Next,two sets of regressions were used to link strategiesto outcomes for each macrostructure independently(i.e., comprehension strategies predicted outcomesfor narratives and expositories separately). Theseanalyses were performed to identify which strate-gies predicted what outcomes. All reported resultsare significant at alpha level p < .05 or better.

Method

Participants

After securing permission from principals at fourelementary schools, teachers sent consent formshome with students of the target age. Parents of 71second- and third-grade children attending fourTitle 1 public schools in two large Midwestern cit-ies provided consent. Forty-two were girls(M = 7.5 years) and 29 were boys (M = 7.8 years).This age was recruited because the children had atleast 1 year of basic reading instruction, resultingin some ability to decode words, a necessary skillfor the original closed caption intervention(Linebarger, Piotrowski, & Greenwood, 2010).Eighty-two percent of the families reported incomesbelow $30,000, 5 children had an identified


reading ⁄ learning disability, and 54.3% of the chil-dren had oral reading rates below a benchmarkindicating risk for reading failure (Good & Kamin-ski, 2002; second-grade at-risk threshold £ 43 wpm,third-grade at-risk threshold £ 76 wpm). Therewere 52.6% at-risk second graders (i.e., at-riskM = 22.3 wpm, all other second graders M =71.2 wpm) and 61.5% at-risk third graders (i.e.,at-risk M = 45.3 wpm, all other third graders M =93.6 wpm). Children were recruited from urbanschools in predominantly low-socioeconomic-status(SES) communities: Fifty-six percent were AfricanAmerican children who spoke English as their firstlanguage and the remaining 44% were Hispanicchildren learning English as a second language (i.e.,English language learners).

Procedure

After obtaining parental consent, all participatingchildren completed a series of pretest assessmentsto measure baseline literacy skills. Following pre-testing, children viewed one episode per visitacross 12 visits. Video stimuli were presented togroups of 2 to 3 children using a 13-in. TV ⁄ VCRcombination unit positioned approximately 1–2 ftaway from them. Experimenters sat to the right andslightly behind the children and were directed tointeract very little except to redirect attention to thescreen in the event that it became necessary. Theviewing took place in empty classrooms during theafter school program hours at each of the schools.After viewing each episode, children participatedin a short, individually administered posttest (i.e.,there were enough research assistants to test allchildren immediately after viewing).

Measures

Program-specific vocabulary knowledge. Vocabularyknowledge was assessed in two ways: word flu-ency and definition knowledge. Children read fivetarget words selected from each video after viewingthe video. All words from each program werecounted, categorized, and selected if they connectedto an episode’s main themes but were not functionwords (e.g., articles, prepositions, auxiliary verbs,participles; Watt, 2007). With these criteria, 60% ofthe words were nouns (e.g., notch, warrior), 27%were verbs (e.g., arrive, locate), and 13% wereadjectives (e.g., thirsty, rude). Examiners scoredchildren’s word reading for accuracy and fluency(4-point scale: 0 = inaccurate, 1 = read with difficulty,2 = sounded out, 3 = fluent; Word Fluency). Examin-

ers were trained and practiced until interexamineragreement on fluency scores equaled 90% or higher.After reading each target word, children were thenasked to provide a definition. If the child wasunable to read the target word, a clear pronuncia-tion was provided prior to asking for the definition.Definitions were scored on a 3-point scale (0 = no orinaccurate response, 1 = partially correct definition,2 = accurate and complete definition; DefinitionKnowledge). Two independent raters scored 11% ofthe definitions. Krippendorff’s alpha indicatedstrong intercoder reliability for all definition scores(a = .93).

Program comprehension. Comprehension ques-tions included a literal and an inferential questionfor each episode. The literal question asked chil-dren to identify a critical story event that was visu-ally and verbally presented (e.g., ‘‘When Xhappened what happened next?’’) while the infer-ential question required the children to provide themain idea of the program (i.e., ‘‘What do you think[program] was trying to teach you?’’). Comprehen-sion questions were scored on a 3-point scale(0 = no ⁄ inaccurate response, 1 = partially correctanswer, 2 = accurate ⁄ complete answer). Two indepen-dent raters scored 11% of the answers. Krippen-dorff’s alpha was acceptable for both literalcomprehension (a = .91) and inferential compre-hension (a = .86).

Baseline vocabulary knowledge and literacy abil-ity. Prior to viewing any videos and after viewingall 12 videos, children read a list of all target words(n = 60 words). Although not the focus of this arti-cle, children demonstrated significant growth invideo-specific word fluency scores from pre- toposttest (i.e., 16.5% growth; Linebarger et al., 2010).General literacy ability was measured using twoDIBELS tasks: Nonsense Word Fluency (NWF) andOral Reading Fluency (ORF; Good & Kaminski,2002). Mean performance was 52.8 letter-soundsper minute (NWF; SD = 29.3) and 48.9 wpm (ORF;SD = 32.1).

Analytical Approach

Each validation outcome (i.e., Word Fluency,Definition Knowledge, Literal Comprehension,Inferential Comprehension) was z-score trans-formed to facilitate comparisons across literacyskills in each relevant analysis. Next, to control forpotential third variables associated with the child’sbaseline literacy ability, a covariate was con-structed by z-score transforming scores on the twopretest DIBELS literacy tasks and then summing


these variables. Because these analyses were takenfrom a larger intervention study evaluating theeffectiveness of closed captions on the literacyskills of two different populations (i.e., low-SESAfrican American children and English languagelearners; Linebarger et al., 2010), we included twocontrols to extract the variance from the relationsamong strategies and child outcomes: interventioncondition (i.e., one group saw all videos with cap-tions while the other saw the same videos withoutcaptions) and the child’s primary language. In pre-liminary analyses, the patterns of results were thesame for both English- and Spanish-speakingchildren (i.e., all results were in the same direction;however, the findings were more robust for Eng-lish language learners); therefore, adopting thisstrategy (i.e., controlling for primary language)appeared reasonable. Gender was unrelated to anyof the outcomes and was dropped from furtheranalyses.

To assess potential dependency issues resultingfrom viewing all 12 videos, two sets of intraclasscorrelations were calculated: between-subjects dif-ferences across children (i.e., .12–.16) or between-subjects differences across video (.92–.94). Largerintraclass correlations indicate that either videos orchildren (depending on the analyses performed)are more likely to share a common experienceresulting in violation of the independence assump-tion. Given the larger variation between videos ver-sus between children, dependency associated withindividual performance across the 12 videos wasminimal (Guo, 2005).

Results and Discussion

Two repeated measures analyses of covariance(ANCOVAs) were computed to examine child out-comes by macrostructure. Table 3 contains means,standard deviations, and ANCOVA results.Although there were no significant differences forliteracy environment codes, it was possible thatmacrostructures would mediate the relationsbetween codes and outcomes; therefore, regressionspredicting outcomes using three sets of codes (i.e.,literacy environment, comprehension strategies,vocabulary strategies) were conducted separatelyfor each macrostructure. Both unstandardized (B)and standardized (b) coefficients from the regres-sion models are found in Table 4. An alpha level of.05 was used for all statistical tests. Only interactionand main effects relevant to the hypotheses are pre-sented next.

The Role of Macrostructure on Comprehension andVocabulary Knowledge

Program comprehension. Based on Study 1 find-ings, we hypothesized that comprehension scoreswould be higher after viewing narratives comparedwith viewing expositories. To test this hypothesis, a2 · 2 (Macrostructure · Comprehension Outcomes)repeated measures ANCOVA with repeated mea-sures on the last factor was computed. Our hypoth-esis was supported. Comprehension was higher fornarratives compared with expositories althoughthe effect was qualified by a significant two-way

Table 3

Means, Standard Deviations, and Analysis of Covariance (ANCOVA) Results for Outcomes Alone and by Macrostructure

Outcomes M SD Range

Word fluency 28.63 25.02 0–120

Definition knowledge 33.44 19.86 0–133

Literal comprehension 1.46 0.85 0–4

Inferential comprehension 1.69 0.68 0–4

Outcomes

Narrative Expository ANCOVA (F-value)

M SE M SE MS O MS · O

Vocabulary knowledge

Word fluency 28.02 0.88 29.25 0.88 4.40* (0.005) 37.21*** (0.042) 21.41*** (0.025)

Definition knowledge 36.23 0.92 30.66 0.92

Comprehension type

Literal 1.64 0.04 1.29 0.04 11.77*** (0.014) 2.29 (0.003) 37.05*** (0.042)

Inferential 1.65 0.03 1.73 0.03

Note. MS = Macrostructure; O = Outcome. Values in parentheses represent partial eta squared (gp2).

*p < .05. ***p < .001.


(Macrostructure · Comprehension Outcomes) inter-action. Literal comprehension was higher afterviewing narratives while inferential comprehensionwas higher after viewing expositories. Univariatetests indicated that differences were significant forliteral comprehension, F(1, 847) = 36.70, gp

2 = .042,and marginally significant (p < .10) for inferentialcomprehension, F(1, 847) = 2.85, gp

2 = .003.Program-specific vocabulary knowledge. Based on

Study 1 findings, we hypothesized that program-specific vocabulary knowledge would be higherafter viewing expositories compared with viewingnarratives. To test this hypothesis, a 2 · 2 (Macro-structure · Vocabulary Outcomes) repeated mea-sures ANCOVA with repeated measures on the lastfactor was conducted. Our hypothesis was not sup-ported. Main effects for Vocabulary Outcomes andMacrostructure were significant. Definition knowl-edge was higher than word fluency while vocabu-lary knowledge overall was higher after viewingnarratives compared with expositories.

As with comprehension, the Macrostructuremain effect was qualified by a significant two-way(Macrostructure · Vocabulary Outcomes) interac-tion. As predicted, word fluency was higher afterviewing expositories. Contrary to our prediction,definition knowledge was higher after viewing nar-ratives. Univariate tests indicated that these differ-

ences were significant for definition knowledge,F(1, 847) = 18.52, gp

2 = .021, and not significant forword fluency, F(1, 847) = 0.98, gp

2 = .001.

What Is the Relation Between the LiteracyEnvironment and Children’s Program Comprehensionand Program-Specific Vocabulary Knowledge byMacrostructure?

Recall that character’s use of and positive com-ments about print and onscreen print were hypoth-esized to create a conducive learning environment.For narratives, two of the three codes predictedoutcomes. Positive statements about print and on-screen print positively predicted literal comprehen-sion and both vocabulary outcomes whilecharacters’ print use supported literal comprehen-sion and suppressed word fluency and definitionknowledge.

Unlike narratives, there were no consistent pat-terns between the literacy environment and out-comes within expositories. Each time charactersused print, definition knowledge scores dropped,literal comprehension improved, and inferentialcomprehension marginally improved. Positivestatements about print supported definition knowl-edge and suppressed word fluency. Onscreen printnegatively predicted definition knowledge, word

Table 4

Regression Coefficients Predicting Outcomes Using Coded Strategies

Narrative regression coefficients Expository regression coefficients

WF DK LC IC WF DK LC IC

Literacy environment

PU )0.79*** ()0.26) )0.93*** ()0.39) 0.03** (0.23) 0.01 (0.05) )0.15 ()0.05) )0.81*** ()0.36) 0.03*** (0.35) 0.006� (0.09)

PS 2.93** (0.24) 3.35*** (0.36) 0.09* (0.21) 0 (0.00) )1.28* ()0.09) 1.49** (0.13) )0.02 ()0.05) ).004 ()0.01)

OP 1.35*** (0.34) 1.98*** (0.64) 0.05*** (0.33) 0.003 (0.02) )0.32*** ()0.26) )0.34*** ()0.34) 0.01** (0.14) ).006*** ()0.19)

R2 0.56*** .25*** 0.26*** 0.01 0.49*** 0.27*** 0.13*** 0.06***

Comprehension strategies

MS )2.31** ()0.17) )3.48*** ()0.34) )0.28*** ()0.58) 0.05 (0.11) )2.53� ()0.24) 11.90*** (1.32) )0.43*** ()1.24) 0.03 (0.10)

PK 0.02*** (0.16) 0.03*** (0.31) 0.0002 (0.06) 0.001* (0.15) 0.03* (0.17) 0.22*** (1.44) )0.006*** ()0.95) 0.0003 (0.08)

SM )0.002 ()0.03) )0.002 ()0.05) )0.0002� ()0.10) )0.0*** ()0.23) 0.01 (0.18) )0.07*** ()1.49) 0.002*** (1.19) 0.00001 (0.01)

QP 0.65*** (0.32) 0.97*** (0.62) 0.03*** (0.38) )0.01* ()0.22) 0.25 (0.13) )1.09*** ()0.69) 0.04*** (0.65) )0.01� ()0.22)

R2 0.57*** 0.27*** 0.24*** 0.05** 0.52*** 0.49*** 0.18*** 0.05**

Vocabulary strategies

TW 0.19 (0.07) 0.36 (0.16) )0.07*** ()0.69) 0.02� (0.25) 0.41 (0.10) )3.64*** ()1.06) 0.12*** (0.87) )0.02 ()0.15)

NR 0.24 (0.07) )0.09 ()0.03) )0.08*** ()0.58) )0.03 ()0.23) )0.53** ()0.29) 1.05*** (0.71) )0.05*** ()0.89) 0.006 (0.13)

SL )1.25 ()0.04) )6.49 ()0.25) 0.25 (0.20) )0.94** ()0.88) )12.98*** ()0.49) 10.37*** (0.49) )0.59*** ()0.71) 0.22� (0.34)

%D )1.76 ()0.18) )0.49 ()0.07) )0.15 ()0.42) 0.37*** (1.21) )4.21*** ()0.53) 2.07** (0.33) )0.13*** ()0.54) 0.01 (0.07)

R2 0.56*** 0.28*** 0.28*** 0.06*** 0.53*** 0.48*** 0.20*** 0.06***

Note. Both unstandardized (B) and standardized (b) regression coefficients are presented. b coefficients are displayed in parentheses.�p < .10. *p < .05. **p < .01. ***p < .001.


fluency, and inferential comprehension and posi-tively predicted literal comprehension.

What Is the Relation Between ComprehensionStrategies and Children’s Comprehension andVocabulary Knowledge by Macrostructure?

There were four comprehension strategies: mainsegments, time spent directing viewers to accessprior knowledge, time spent summarizing a mainsegment, and total questions ⁄ predictions used tohighlight upcoming content. Results were fairlyconsistent for narratives; that is, most coefficientswere in the same direction (although some wereunrelated) across outcomes. As main segmentsincreased, literal comprehension and both vocabu-lary scores decreased. Spending time activatingprior knowledge predicted higher vocabulary andinferential comprehension while time spent sum-marizing main content suppressed literal (margin-ally) and inferential comprehension. As characters’questions and predictions increased, vocabularyperformance and literal comprehension increasedwhile inferential comprehension decreased.

For expositories, the results were less consistent.As main segments increased, word fluency and lit-eral comprehension suffered while definitionknowledge improved. Activating prior knowledgeboth positively predicted vocabulary outcomes andnegatively predicted literal comprehension. In con-trast, longer summaries predicted lower definitionknowledge and higher literal comprehension. Asthe number of questions and predictions increased,definition knowledge and inferential comprehen-sion decreased and literal comprehensionincreased.

What Is the Relation Between Vocabulary Strategiesand Children’s Program Comprehension and Program-Specific Vocabulary Knowledge by Macrostructure?

Vocabulary strategies consisted of four codedvariables: frequency of target word use, averagesentence length, percentage of Dolch words, andthe narration rate. Interestingly, for narratives, noneof the vocabulary strategies predicted word fluencyor definition knowledge. Literal comprehensiondeclined as repetition of target words and the nar-ration rate increased. Inferential comprehensiondeclined as sentence length increased while itimproved as the percentage of Dolch wordsincreased.

For expositories, the role of vocabulary strategieswas much more prominent. Word fluency scores

decreased as the narration rate, the sentence lengthand the percentage of Dolch words increased. Defi-nition knowledge scores decreased with each repe-tition of a target word and increased with fasternarration rates, longer sentences, and a higher per-centage of Dolch words. Literal comprehensionincreased with each repetition of a target word, anddecreased with faster narration rates, longer sen-tences, and a smaller percentage of Dolch words.Inferential comprehension scores marginallyincreased with longer sentences.

General Discussion

In Study 1, a set of learning strategies were identi-fied and coded across two episodes of six children’sTV programs. Programs were further categorizedusing one of two macrostructures: narrative orexpository. In Study 2, working with a populationof children from low-SES homes at risk for literacydifficulties, we evaluated the general effects associ-ated with watching content presented within eachmacrostructure as well as whether and how thecoded strategies embedded in each macrostructurepredicted vocabulary and comprehension.

The Role of Narrative and Expository Macrostructures

Study 1 established that narrative macrostruc-tures contained more learning strategies hypothe-sized to support comprehension outcomes whileexpository macrostructures contained more of bothsupportive and unsupportive strategies hypothe-sized to predict vocabulary outcomes. We furtherhypothesized that narratives would predict stron-ger comprehension performance while expositorieswould predict stronger vocabulary performance.Study 2 partially confirmed these hypotheses.When viewing narrative macrostructures, all out-comes were higher compared with outcomes whenviewing expository macrostructures.

Programs using narrative macrostructures tellstories with a beginning, middle, and end; use char-acters; include plot or conflict; and create a setting.In other words, they present content in a structur-ally homogenous way using prototypical storyparts. Narrative macrostructures should reduceprocessing demands due to children’s considerablenarrative experience in real-life, print-based, andTV-based contexts (Fisch, 2004; Linebarger & Pio-trowski, 2009). This narrative experience likelytranslated into both sensitivity to and automaticitywith the structures typical of narratives, making


children faster and more competent at identifyingstory events (i.e., literal comprehension), filling ingaps in presented content, and freeing up valuablecognitive resources to make connections to real-lifesituations or broader themes (i.e., inferential com-prehension; Smith & Ragan, 2005). As predicted,identification of literal story events was stronger fornarrative macrostructures. Although narrativeswere expected to make it easier to refocus cognitiveresources toward making broader connections, chil-dren infrequently did this (Linebarger, 2001). Previ-ously, deep processing was more likely to occur ifcontent was moderately novel and of intermediatecomplexity while superficial processing occurredwith content that was overly familiar or excessivelychallenging (Rice et al., 1982; Roller, 1990). Withexperience, processing narratives becomes auto-matic and children are more likely to allocate mini-mal resources to them.

Unlike the prototypical format of narratives, pro-grams that use an expository macrostructure pres-ent content in structurally heterogeneous ways(Best et al., 2008). Based on Study 1, watchingexpositories was expected to predict strongervocabulary outcomes when compared with narra-tives because expositories used more supportivevocabulary strategies. Contrary to this prediction,vocabulary performance (particularly definitionknowledge) was higher after viewing narrativesversus expositories. The structural heterogeneityinherent in expositories offers an explanation.When processing unfamiliar content deliveredthrough an expository macrostructure, childrenmust simultaneously process the new content andthe general purpose of an expository text to effec-tively learn the content. Duke and Kays (1998)contend that young children can learn from exposi-tory texts but, because they are not consistentlyexposed to these formats until as late as fourthgrade, learning from expository texts is difficultand frustrating.

The Role of Learning Strategies

In Study 1, three categories of learning strategiesembedded in children’s educational TV were iden-tified and coded: strategies depicting a supportiveliteracy environment, strategies supporting compre-hension, and strategies supporting vocabulary.Despite selecting strategies with evidence of theireffectiveness in other contexts, the hypothesizedeffects were not uniformly present. Instead, rela-tions between strategy and outcome differed bystrategy type, program macrostructure, specific

child outcome, or some combination of these threefactors. Refocusing interpretation to the cognitiveactivity generated by a strategy clarified the incon-sistent findings. Smith and Ragan (2005) proposedfive broad types of learning strategies: organizing,rehearsing, elaborating, metacognitive, and affec-tive. The first four strategies elicit specific cognitiveactivity while affective strategies create environ-ments that trigger attention to and increasedengagement with content. No metacognitive strate-gies were coded in this study.

An additional layer of complexity was intro-duced by the macrostructures used to deliver con-tent. As described, macrostructures introduce aframework for content through their use of particu-lar and predictable learning strategies that arrangeideas and make connections among these ideas.Narrative macrostructures use a fairly uniform setof strategies and children’s accumulated experi-ences with narratives are substantial. Coupling pre-dictability with experience suggests that narrativesmay be better macrostructures for delivering con-tent, especially when that content is unfamiliar orcomplex. Unlike narratives, expository macrostruc-tures are highly variable and dependent on the pur-pose of a program or the unique characteristics ofparticular content. Expositories can include strate-gies that describe a topic, present sequential orchronological information, introduce cause andeffect, or highlight similarities and differences.Because young children have little experience withexpository forms, learning content embedded withthese forms may prove challenging. Recent evi-dence suggests that introducing expository text inpreschool or early elementary school can help chil-dren become familiar with a range of expositoryforms and subsequently mitigate difficulties thatarise when children shift from learning to read toreading to learn around fourth grade (Duke &Kays, 1998).

Organizing strategies structured content byencasing it within a common framework and byusing simple supporting text to increase attentionto conceptual content (Smith & Ragan, 2005). In thisstudy, simple supporting text was defined by morehigh-frequency words (i.e., Dolch words), longerbut less complex sentences (sentence length), andnarration rates that did not exceed the averagewpm that children this age are able to adequatelyprocess (i.e., narration rate; Barron, 2004). Thesestrategies worked in concert to increase or decreasethe volume of content contained in an episode.Greater use of main segments was associated withmore unique content ideas that structured the


presentation and flow of that content. As main seg-ments increased, the narration rate sped up consid-erably while the sentence length modestlyincreased. Sentence length was more highly relatedto Dolch words, suggesting that sentences becamelonger by using more high-frequency words whilenarration rates became faster due to more contentwords. The latter relation indicates cognitive over-load.

Narratives with more main segments negativelypredicted word fluency, definition knowledge, andliteral comprehension likely because extra main seg-ments were indicative of more irrelevant subplots,making it hard to keep the plot points together.Both vocabulary outcomes and literal comprehen-sion suffered as a result. These difficulties werecompounded when dialogue was spoken faster andsentence lengths were longer. Simplifying the con-tent came at a cost as definition knowledge suf-fered. Without key conceptual content or extendeddiscussions about particular concepts, viewers wereable to provide limited definitions. Inference gener-ation was supported with shorter dialog segmentsfilled with more high-frequency words.

Expositories contained more main segments,longer sentences, more Dolch words, and fasternarration rates. As a result, word fluency and literalcomprehension suffered. In contrast, the conceptualunderstanding needed to provide solid word defi-nitions was supported by more volume. Main seg-ments tended to build on each other throughout anexpository episode. The three expositories used inthis study contained multiple examples anddescriptions that linked to the theme of thatepisode. For example, in RR, one episode abouthumpback whales featured multiple vignettes thatdefined this type of whale from several viewpoints,including a scientist, a whale-watching boatcaptain, the host, and a nonfiction book.

Rehearsing strategies directly supported theencoding and retrieval of information by providingmultiple opportunities for practice. Coded strate-gies were repetition of target words and repetitionof main segment content (i.e., summaries). Whencontent is repeated, it signals that it is importantand should be attended to. Repeating target wordsis a fairly simple rehearsal tool while providingmain segment summaries is a more complexrehearsal strategy.

When narratives used more rehearsal strategies,comprehension was better. Children’s experienceand facility processing narratives freed upresources to devote to comprehending content. Byspending time repeating content, children were

better able to identify story events (i.e., literal com-prehension) and, more important, generate infer-ences about these events.

Rehearsal strategies embedded in expositorymacrostructures aided acquisition of definitionknowledge and, to a lesser extent, literal compre-hension. Expositories contained noticeably morecontent than narratives. To ensure adequate com-prehension of vocabulary, expositories includedmultiple rehearsal opportunities (i.e., words aver-aged 27 repeats and summaries averaged 8 min).Interestingly, summaries were almost 2 min longerin narratives, a finding that may explain why out-comes were higher after viewing narratives.

Elaborating strategies help learners create linksbetween new content and existing prior knowledge.Two strategies elicited this cognitive activity: timespent activating viewers’ prior knowledge and totalquestions and predictions made about upcomingcontent. Both strategies enabled viewers to establishassociations between new information and priorknowledge, to identify key concepts or story events,and to increase the personal relevance of thecontent.

When narratives used more elaboration, all out-comes were higher especially vocabulary. As withthe other cognitive strategy types, when used in anarrative macrostructure, children were able todevote their attention to conceptual informationand story events rather than needing to split timesimultaneously to make sense of the presentationformat and the content.

Elaboration strategies used in expositories pre-dicted more definition knowledge and inferentialcomprehension. Elaborating is a demanding cogni-tive activity because it requires that children under-stand program content, possess knowledge abouthow content links to the macrostructure, and con-nect content and strategy to relevant backgroundknowledge. Using too many elaborating strategiesimpeded literal comprehension likely because theydirected attention to conceptual understanding andaway from key events. In the comprehension hier-archy, knowing story events is less important overtime while generating inferences or themes is moreimportant.

Frequently using affective strategies shouldinfluence engagement with and motivation towardattending to content resulting in a more favorablelearning environment. Three affective strategieswere hypothesized to create such an environment:characters using print, characters making positivestatements about print, and the display of onscreenprint. These strategies were conceptually linked to


three instructional conditions found to influencelearning: demonstration of the desired behaviorsby a respected role model, opportunities for prac-tice using those behaviors, and reinforcement ofthose behaviors (Gagne, 1985; Smith & Ragan,2005).

Narratives supported program-specific learning(i.e., word fluency, definition knowledge, literalcomprehension) when characters made positivestatements about print or text was displayed onsc-reen. Characters using print hindered literal com-prehension and both vocabulary outcomes. Thecharacters using print may not be likeable or credi-ble or there may have been too little explicit rein-forcement of the benefits of print (Gagne, 1985;Smith & Ragan, 2005). These explanations are sup-ported by the positive relation between positivestatements and outcomes. Onscreen print providedopportunities for reading practice and elaborationby providing visual and aural cues that makeprint more salient and easier to decode. While theliteracy environment helped children learn pro-gram-specific content, this learning did not transferto inferencing.

Connections between affective strategies andoutcomes were less consistent for expositories.Modeling print use predicted both comprehensionoutcomes but impeded definition knowledge whilepositive statements predicted stronger definitionknowledge and impeded word fluency. Onscreenprint supported literal comprehension only. Theremaining three outcomes were diminished bymore onscreen print. These inconsistencies arelikely due to a combination of cognitive overloadand gaps in knowledge brought on by unfamiliarforms and content.

Conclusion

The results of the two studies indicate thatmacrostructures and learning strategies must beconsidered simultaneously to understand howchildren learn from televised content. To facilitatelearning, programs should generate cognitiveactivity that takes advantage of the broader pre-sentation framework along with particular strate-gies that lead to learning when used in thisframework. Over time and with experience, pro-cessing demands shift due to stimulus-specificand person-specific factors. Stimulus-specific fac-tors included macrostructures and learning strate-gies used to convey content and generatecognitive activity. In contrast, person-specific fac-tors included experience with macrostructures

(particularly for expository texts) and accumulatedbackground knowledge about the content and itspresentation form. While children typically havemore accumulated experiences with narratives,they can, when taught to do so, learn equally wellfrom expository macrostructures. TV provides aunique opportunity to familiarize children withexpository structures. There is clear evidence thatTV comprehension is linked to reading compre-hension; that is, TV comprehension was highlycorrelated with print comprehension in preschool(Linebarger & Piotrowski, 2009) and predictive ofconventional reading success longitudinally (i.e.,TV comprehension at 6 years predicted readingcomprehension at 8 years; Kendeou et al., 2005).General processing skills used across media sharemany commonalities. As such, educational TV canplay a vital role in preparing at-risk children inlow-SES families to read.

References

Anderson, D. R., Huston, A. C., Schmitt, K. L., Linebar-ger, D. L., & Wright, J. C. (2001). Early childhood tele-vision viewing and adolescent behavior: The RecontactStudy. Monographs of the Society for Research in ChildDevelopment, 66(1), 1–143.

Bandura, A. (2001). Social cognitive theory of mass com-munication. Media Psychology, 3, 265–299.

Barron, A. E. (2004). Auditory instruction. In D. H. Jonas-sen (Ed.), Handbook of research in educational communica-tions and technology (pp. 949–978). Mahwah, NJ: Erlbaum.

Best, R., Floyd, R., & McNamara, D. (2008). Differentialcompetencies contributing to children’s comprehensionof narrative and expository texts. Reading Psychology,29, 137–164.

Crawley, A. M., Anderson, D. R., Wilder, A., Williams,M., & Santomero, A. (1999). Effects of repeated expo-sure to a single episode of the television program Blue’sClues on the viewing behaviors and comprehension ofpreschool children. Journal of Educational Psychology, 91,630–637.

Duke, N. K., & Kays, J. (1998). ‘‘Can I say ‘once upon atime?’’’: Kindergarten children developing knowledgeof information book language. Early Childhood ResearchQuarterly, 13, 295–318.

Fisch, S. M. (2004). Children’s learning from educational tele-vision. Mahwah, NJ: Erlbaum.

Gagne, R. (1985). The conditions of learning and theory ofinstruction (4th ed.). New York: Holt, Rinehart & Winston.

Good, R. H., & Kaminski, R. A. (2002) Dynamic indicatorsof basic early literacy skills (DIBELS). Eugene, OR: Insti-tute for the Development of Educational Achievement.Retrieved February 11, 2008, from http://dibels.uoregon.edu/


Guo, S. (2005). Analyzing grouped data with hierarchicallinear modeling. Children and Youth Services Review, 27,637–652.

Hayes, A. F., & Krippendorff, K. (2007). Answering thecall for a standard reliability measure for coding data.Communication Methods and Measures, 1, 77–89.

Herrman, D. J., Yoder, C., Gruneberg, M., & Payne, D.(2006). Applied cognitive psychology: A textbook. Mahwah,NJ: Erlbaum.

Jensema, C. J., McCann, R., & Ramsey, S. (1996). Closed-captioned television presentation speed and vocabu-lary. American Annals of the Deaf, 141, 284–292.

Kendeou, P., Lynch, J. S., van den Broek, P., Espin, C. A.,White, M. J., & Kremer, K. (2005). Developing success-ful readers: Building early comprehension skillsthrough television viewing and listening. Early Child-hood Education Journal, 33, 91–98.

Linebarger, D. L. (2001). Learning to read from television:The effects of using captions and narration. Journal ofEducational Psychology, 93, 288–298.

Linebarger, D. L., Kosanic, A. Z., Greenwood, C. R., &Doku, N. S. (2004). Effects of viewing the televisionprogram Between the Lions on the emergent literacyskills of young children. Journal of Educational Psychol-ogy, 96, 297–308.

Linebarger, D. L., & Piotrowski, J. T. (2009). TV as Story-teller: How exposure to television narratives impacts at-risk preschoolers’ story knowledge and narrative skills.British Journal of Developmental Psychology, 27, 47–69.

Linebarger, D. L., Piotrowski, J. T., & Greenwood, C. R.(2010). Onscreen print: The role of closed captionsas a supplemental literacy tool. Journal of Research inReading, 33(2), 148–167.

Linebarger, D. L., & Walker, D. (2005). Infants’ andtoddlers’ television viewing and relations to languageoutcomes. American Behavioral Scientist, 46, 624–645.

May, F. (1998). Reading as communication: To help childrenwrite and read. Upper Saddle River, NJ: Merrill.

McMahon, M. L. (1983). Development of reading-while-listening skills in the primary grades. Reading ResearchQuarterly, 19, 38–52.

Michel, E., & Roebers, C. M. (2008). Children’s knowl-edge acquisition through film: Influence of programmecharacteristics. Applied Cognitive Psychology, 22, 1228–1244.

Naigles, L., & Mayeux, L. (2001). Television as incidentallanguage teacher. In D. G. Singer & J. L. Singer (Eds.),Handbook for children and the media (pp. 135–152). Lon-don: Sage.

National Institute of Child Health and Human Develop-ment. (2000). Report of the National Reading Panel. Teach-ing children to read: An evidence-based assessment of thescientific research literature on reading and its implicationsfor reading instruction (NIH Publication No. 00-476).Washington, DC: U.S. Government Printing Office.

Nielsen Media Research. (2003). 2002 ⁄ 03 kids programmingAA kids 2-5 rating ranker. New York: Nielsen MediaResearch.

Nord, C. W., Lennon, J., Liu, B., & Chandler, K. (1999)Home literacy activities and signs of children’s emergingliteracy, 1993 and 1999. Washington, DC: U.S. Depart-ment of Education, Office of Educational Research andImprovement. Retrieved June 28, 2007, from http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2000026

Patterson, J. L. (2002). Relationships of expressive vocabu-lary to frequency of reading and television experienceamong bilingual toddlers. Applied Psycholinguistics, 23,493–508.

Reinking, D., & Schreiner, R. (1985). The effects of com-puter-mediated text on measures of reading compre-hension and reading behavior. Reading ResearchQuarterly, 20, 536–552.

Renninger, K. A. (1992). Individual interest and develop-ment: Implications for theory and practice. In K. A.Renninger, S. Hidi, & A. Krapp (Eds.), The role of inter-est in learning and development (pp. 361–395). Hillsdale,NJ: Erlbaum.

Rice, M. L., Huston, A. C., & Wright, J. C. (1982). Theforms of television: Effects on children’s attention,comprehension, and social behavior. In D. Pearl, L.Bouthilet, & J. B. Lazar (Eds.), Television and behavior: Tenyears of scientific progress and implications for the 80’s (Vol.2, pp. 24–38). Washington, DC: Government PrintingOffice.

Rice, M. L., & Woodsmall, L. (1988). Lessons from televi-sion: Children’s word learning when viewing. ChildDevelopment, 59, 420–429.

Roller, C. M. (1990). The interaction between knowledgeand structure variables in the processing of expositoryprose. Reading Research Quarterly, 25, 79–89.

Sadoski, M., & Paivio, A. (2001). Imagery and text: A dualcoding theory of reading and writing. Reading and Writ-ing, 16, 259–262.

Scarborough, H. S., & Dobrich, W. (1994). On the efficacyof reading to preschoolers. Developmental Review, 14,245–302.

Schiefele, U. (1998). Individual interest and learn-ing—What we know and what we don’t know. In L.Hoffman, A. Krapp, & K. A. Renninger (Eds.), Proceed-ings of the Seeon Conference on Interest and Gender (pp.91–104). Kiel, Germany: IPN.

Smith, P., & Ragan, T. (2005). Instructional design (3rded.). Hoboken, NJ: Wiley Jossey-Bass.

Snow, C. E., Burns, M. S., & Griffin, P. (Eds.). (1998). Pre-venting reading difficulties in young children. WashingtonDC: National Academy Press.

Stein, N. L., & Trabasso, T. (1982). What’s in a story? Anapproach to comprehension. In R. Glaser (Ed.),Advances in the psychology of instruction (pp. 213–268).Hillsdale, NJ: Erlbaum.

Szabo, M., & Lamielle-Landy, A. (1981). Television-basedreading instruction, reading achievement, and taskinvolvement. Journal of Educational Research, 74, 239–244.

Taylor, S. E. (1964). Listening: What research says to the tea-cher. Washington, DC: National Education Associationof the United States.


Taylor, B. M., Peterson, D. S., Pearson, P. D., & Rodri-guez, M. C. (2002). Looking inside classrooms:Reflecting on the ‘‘how’’ as well as the ‘‘what’’ ineffective reading instruction. The Reading Teacher, 56,270–279.

Uchikoshi, Y. (2005). Narrative development in bilingualkindergarteners: Can Arthur help? Developmental Psy-chology, 41, 464–478.

Watt, R. J. C. (2007). Concordance software. RetrievedAugust 28, 2007, from http://www.concordancesoft-ware.co.uk/

Wright, J. C., Huston, A. C., Murphy, K. C., St. Peters, M.,Pinon, M., Scantlin, R., et al. (2001). The relations ofearly television viewing to school readiness and vocab-ulary of children from low income families: The earlywindow project. Child Development, 72, 1347–1366.


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