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Levels of planning predict different reading comprehension outcomes
LEAIND-01257; No of Pages 5 Learning and Individual Differences xxx (2016) xxx–xxx
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Learning and Individual Differences journal homepage: www.elsevier.com/locate/lindif
Levels of planning predict different reading comprehension outcomes J.P. Das, George K. Georgiou ⁎ University of Alberta, Edmonton, Canada
a r t i c l e
i n f o
Article history: Received 28 July 2015 Received in revised form 2 January 2016 Accepted 2 April 2016 Available online xxxx Keywords: Planning Reading comprehension Activity theory Crack the Code Adults
a b s t r a c t The purpose of this study was to examine if different measures of Planning could be used to operationalize action and operation planning, and if these two levels of Planning could predict different reading comprehension outcomes after controlling for vocabulary and reading speed. One hundred eighty-six university students (120 females; mean age = 22.26 years) were assessed on Planning (Planned Connections, Planned Patterns, Planned Codes, and Crack the Code), vocabulary, reading speed, and reading comprehension (Nelson-Denny Reading Test and Interleaved Sentences). The results of factor analysis indicated first that the four Planning tasks were loading on two separate factors conceptually dividing them into two levels: action planning (CTC) and operation planning (Planned Connections, Planned Patterns, and Planned Codes). Second, the results of regression analyses showed that action planning was accounting for unique variance only when predicting performance demanding a higher level of comprehension (Nelson Denny), whereas operation planning predicted performance only in a task requiring a relatively lower level of comprehension (Interleaved Sentences). These findings suggest that the division of Planning into its different levels can help us better understand its contribution to reading comprehension. At the same time, the cognitive demands exerted by different comprehension outcomes may also determine the strength of their relationship with Planning. © 2016 Elsevier Inc. All rights reserved.
1. Introduction Cognitive processes such as Planning are highly relevant in our daily life (e.g., Das, Kar, & Parrila, 1996; Luria, 1966) and key predictors of academic success (e.g., Best, Miller, & Naglieri, 2011; Friedman et al., 2014; Naglieri & Rojahn, 2004; Papadopoulos, Parrila, & Kirby, 2015). Planning is defined as “any hierarchical process in the organism that can control the order in which a sequence of operations is to be performed” (Miller, Galanter, & Pribram, 1960; p. 16). According to Das and Misra (2015), it is the plan that controls human information processing and supplies patterns for essential connections between knowledge, evaluation, and action. In this brief report, we aimed to delineate different levels of Planning by providing tests that could operationalize them and by examining how these levels may relate to reading comprehension. Initially, Luria (1966) identified Planning as one of the three functional units in the brain associated with the functioning of the frontal lobe, especially of the prefrontal cortex. The prefrontal cortex plays a central role in forming goals and objectives and then in devising plans of action required to attain these goals (Miller & Cohen, 2001). Following Luria's pioneer work, Das and colleagues (see e.g., Das et al., 1996; Das, Naglieri, & Kirby, 1994; Naglieri & Das, 1987) conceptualized Planning as one of the major cognitive processes in the PASS (Planning, ⁎ Corresponding author at: Department of Educational Psychology, 6-102 Education North, University of Alberta, Edmonton, AB T6G 2G5, Canada. E-mail address: [email protected] (G.K. Georgiou).
Attention, Simultaneous and Successive processing) theory of intelligence. More recently, Planning has been subsumed under the umbrella of Executive Functions (EF) along with inhibition and working memory (e.g., Clark, Pritchard, & Woodward, 2010; Lehto, Juujärvi, Kooistra, & Pulkkinen, 2003). As such, several studies have shown that complex Planning tasks such as Tower of London and Wisconsin Card Sorting can predict reading (e.g., Sesma, Mahone, Levine, Eason, & Cutting, 2009) and mathematics (e.g., Bull, Espy, & Wiebe, 2008). Finally, Best et al. (2011) have attempted to build bridges between EF and PASS theory by proposing that the three measures of Planning in the DasNaglieri Cognitive Assessment System (D-N CAS; Naglieri & Das, 1997) can be used to operationalize “complex” EF. We provide below a nuanced view of levels of planning that conceptually divide tests for the assessment of Planning. 1.1. Levels of planning Three different levels of Planning can be distinguished within a broad framework adopted from activity theory (Leontjev, 1978). These are activity planning, action planning, and operation planning. As proposed by Leontjev (1978), at the level of activity, Planning can be conceptualized as a method of realizing or aiming towards one's general life goals and motives. The function of activity planning is to mediate between a person's life goals and the external world. In turn, action planning is equivalent to problem solving. It aims at achieving a particular goal or solving a particular problem. It involves forming a mental representation of the problem, the constraints on planning, and the
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Please cite this article as: Das, J.P., & Georgiou, G.K., Levels of planning predict different reading comprehension outcomes, Learning and Individual Differences (2016), http://dx.doi.org/10.1016/j.lindif.2016.04.004
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course of action. Action planning is especially important in tasks where finding the solution requires integration of multiple steps into a coherent process and in tasks that allow more than one way to find the answer. The Crack the Code (CTC) task fits nicely this description (Das et al., 1996). CTC is based on the popular Mastermind game and was developed in 1983 by J. P. Das (Das & Heemsbergen, 1983). Since then, it has been used in several studies (Das, Mensink, & Janzen, 1990; Papadopoulos, Panayiotou, Spanoudis, & Natsopoulos, 2005; Parrila, Das, & Dash, 1996; Parrila, Papadopoulos, & Mulcahy, 1997). Importantly, using factor analysis, Parrila et al. (1996) showed that CTC was somewhat distant from other Planning tasks. This allowed us to suggest that CTC is at a level of activity distinct from other planning tasks. In CTC, participants are shown two to five lines of information that contain three to five colored chips that are in a particular order (see Fig. 1). They are also shown a label indicating how many of the colored chips are in their correct place. The participant's task is to integrate information from all of the information lines and place his/ her set of colored chips on the answer line in such an order that all the information lines are true. At the level of operation, Planning is equivalent to strategies and tactics, and consists of working towards the solution of a problem in accordance with task-imposed constraints. Because the goal or end result is often known, operation planning involves forming a representation of the task and conditions, choosing the possible operations to be applied, and then executing these steps. We suggest that the Planning tasks of the D-N CAS (Naglieri & Das, 1997) engage operation planning (see also Parrila et al., 1996, for empirical evidence). For example, in Planned Codes, individuals know that the end goal is to fill in as many empty boxes as possible with a combination of Os and Xs that corresponds to a given letter. Once they create a representation of the problem, they choose the strategy that will help them achieve their goal and follow the same strategy until the completion of the task. 1.2. Planning and reading comprehension To date, only a handful of studies have examined the role of Planning in reading comprehension and they have provided mixed findings (e.g., Best et al., 2011; Kendeou, Papadopoulos, & Spanoudis, 2015; Locascio, Mahone, Eason, & Cutting, 2010; Naglieri & Rojahn, 2004; Sesma et al., 2009). On the one hand, studies that examined the role of PASS processes in reading comprehension have generally shown that Planning is not a unique predictor of comprehension, when viewed in conjunction with Attention, Simultaneous, and Successive processes (e.g., Georgiou & Das, 2014; Naglieri & Rojahn, 2004; however see also Kendeou et al., 2015). On the other hand, studies investigating the role of Planning (as part of EF) have typically reported significant effects
on reading comprehension (e.g., Cutting, Materek, Cole, Levine, & Mahone, 2009; Latzman, Elkovitch, Young, & Clark, 2010; Locascio et al., 2010; Sesma et al., 2009). For example, Sesma et al. (2009) found that 9- to 15-year-old poor comprehenders had significant deficits in planning and working memory, even when the effects of word-reading skill and vocabulary were controlled for. In Sesma and colleagues' study, Planning was assessed with the Tower of London task. A possible explanation for these conflicting findings could be sought in the different levels of Planning. When operation planning tasks are used (as in D-N CAS), Planning exerts a limited role in reading comprehension. In contrast, when action planning tasks are used, Planning seems to predict reading comprehension. However, an alternative explanation could be sought to the kind of reading comprehension outcomes used. Recent findings suggest that different comprehension tests have different cognitive processing demands (e.g., Kendeou, Papadopoulos, & Spanoudis, 2012; Papadopoulos, Kendeou, & Shiakalli, 2014). Most studies on PASS processes and reading comprehension operationalized the latter with Woodcock-Johnson Passage Comprehension (WJPC). WJPC has been found to be strongly dependent on decoding (Keenan, Betjemann, & Olson, 2008) and working memory (Kendeou et al., 2012). In contrast, Sesma et al. (2009) used the Wechsler Individual Achievement Test-II reading comprehension task that requires not only decoding, but also inference generation and vocabulary knowledge. 2. The present study The purpose of this brief report was to examine if old (Planned Connections, Planned Codes) and newer (Planned Patterns and CTC) Planning tasks can be used to operationalize two different levels of Planning (action planning and operation planning) following Leontjev's (1978) conceptualization and whether the two levels of Planning can predict different reading comprehension measures, after controlling for the effects of vocabulary and reading speed. We hypothesized that Planned Connections, Planned Patterns, and Planned Codes will operationalize operation planning and CTC will operationalize action planning. In addition, we hypothesized that comprehension at a higher level (assessed with Nelson-Denny Reading Test, see below for details) would be like a problem solving task and would involve more of action planning. In contrast, comprehension at a lower level (assessed with Interleaved Sentences, see below for details), would involve more of operation planning. 3. Method 3.1. Participants One hundred eighty-six undergraduate students (120 females and 66 males; Mean age = 22.26 years, SD = 4.07) from the University of Alberta (Canada) participated in the study. The participants were Caucasian and reported English as their native language. All students registered full-time in their studies and received credit in an introductory educational psychology course for their participation. Students who were serviced by Specialized Support and Disabilities Services for severe disabilities were excluded from the study. Written consent was obtained prior to testing. 3.2. Measures
Fig. 1. Item 5 from Crack the Code task.
3.2.1. Action planning Crack the Code (CTC; Das & Heemsbergen, 1983; see also Das & Misra, 2015, for the items) was used to assess action planning. CTC requires participants to determine what the correct sequence of colored chips is when limited amount of information is provided in the instruction lines (see the example in the Introduction). The version used in this study consisted of six items. The six items can be divided into three pairs
Please cite this article as: Das, J.P., & Georgiou, G.K., Levels of planning predict different reading comprehension outcomes, Learning and Individual Differences (2016), http://dx.doi.org/10.1016/j.lindif.2016.04.004
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of formally similar, but progressively more difficult items. In the first two items, two information lines and three colored disks (blue, black, and white) were used. Items 3 and 4 consisted of four disks (blue, black, white, and yellow) and four information lines, and Items 5 and 6 had four disks and three information lines. Despite having fewer information lines, Items 5 and 6 are more difficult because they have fewer matching placements in different information lines, and thus, less readily detectable constraints for the planned moves. A discontinuation rule of two consecutive errors was applied. The experimenter recorded the accuracy of each item. Cronbach's alpha reliability coefficient in our sample was 0.78. 3.2.2. Operation planning Three measures of operation planning were administered: Planned Connections, Planned Codes, and Planned Patterns. The first two tasks were adopted from the D-N CAS (Naglieri & Das, 1997) and have been used in previous studies showing good reliability and validity evidence (e.g., Das et al., 2007; Naglieri & Rojahn, 2004). In Planned Connections, participants were asked to connect sequential stimuli. In Items 1 and 2, participants were asked to connect numbers (1 to 25) that were semirandomly arranged on a page. In Item 3, participants were asked to connect 25 numbers (1–25) and 25 letters (A–Z) in successive order (1, A, 2, B, 3, C). The participant's score was the total time to complete all three items. Test-retest reliability coefficient in a sub-sample of our participants (n = 45) was 0.83. In Planned Codes, participants were asked to fill in as quickly as possible, and in any manner they would choose, empty boxes with a combination of Os and Xs that corresponded to a letter (e.g., A = OX; B = XX; C = OO; D = XO) that was printed on top of each empty box. The task contained two pages, each with a distinct set of codes. At the top of each page there was a legend, which was indicating the combination of Os and Xs that corresponded to each letter. The participants were allowed 1 min to fill in as many empty boxes as possible. The time and number correct for each page was recorded and the participants' score was calculated by first dividing the number correct in each page by the time and then by averaging the scores of the two pages. Test-retest reliability coefficient in a sub-sample of our participants (n = 45) was 0.87. Finally, in Planned Patterns (see Das & Misra, 2015, for the items), the participants were shown pages with boxes arranged in four rows and four columns. The boxes contained numbers in each of their corners that were creating a pattern (N or Z) if viewed in ascending order (from smaller to larger). In the first two items, the participants were asked to say N or Z depending on the pattern that was created by the numbers. In the third item, the participants were asked to say N every time they saw Z and Z every time they saw N. The time to name each page of boxes was recorded and the participant's score was the total time to complete all three pages. The test is a Luria-type test of Planning (Luria, 1966) similar in principle to verbal regulation of reciprocal response: ‘When I show my palm, you respond by showing me your fist; when I show my fist, you show me your palm’. Test-retest reliability coefficient in a subsample of our participants (n = 45) was 0.80. 3.2.3. Vocabulary Peabody Picture Vocabulary Test (PPVT-IV; Dunn & Dunn, 2007) was used to assess receptive vocabulary. In this task, the examiner presented a series of pictures to each participant. There were four pictures on each page and each picture was numbered. The examiner was saying a word describing one of the pictures and asked the participants to point to or say the number of the picture that the word described. The task was discontinued after 10 errors in a set of 12 items. The participant's score was the total number of correct items. Cronbach's alpha reliability coefficient in our sample was 0.90. 3.2.4. Reading speed Text reading speed was assessed with the Nelson-Denny Reading Test (Brown, Fishco, & Hanna, 1993). The participants were asked to
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read silently the first passage in the test and put a mark to the right of the line they were reading after one minute. The minimum reading rate score in the task is 007 and the maximum 601. Test-retest reliability coefficient in a sub-sample of our participants (n = 45) was 0.81. 3.2.5. Reading comprehension Reading comprehension was assessed with two measures: NelsonDenny Reading Test (Brown et al., 1993) and Interleaved Sentences (Das & Misra, 2015). In Nelson-Denny Reading test, participants were asked to read each one of the seven passages in the test and answer the multiple choice questions following each passage. The test contained 38 multiple-choice questions, each with five answer choices. The time limit to complete the test was 20 min. A participant's score was the total number of correct answers. Cronbach's alpha reliability coefficient in our sample was 0.92. Interleaved Sentences essentially involved reconstructing narration of a story (see Das & Misra, 2015). Participants were presented with a set of four sentences and were asked to read the sentences, put a slash in between sentences, and rank order them so that the passage would make sense (e.g., one night a bear came to the house/a brother and a sister live near the woods/they got really scared and hide under the bed/the sister likes to bake apple pies and leaves them on the front stairs to cool down/). Prior to timed testing, participants were given two practice items to ensure they understood the instructions. The test consisted of six items. Because the sentences were relatively easy, accuracy was high (96%) and was not considered further. The participant's score was the average time to complete all six items. Test-retest reliability coefficient in a sub-sample of our participants (n = 45) was 0.85. 3.3. Procedures All tests were individually administered by two graduate students who received extensive training on test administration. Testing lasted approximately an hour and took place in a quiet room at the university. The tasks were administered in the following order: Nelson-Denny Reading Test, Planned Connections, Planned Patterns, Planned Codes, Crack the Code, Vocabulary, Text Reading Speed, and Interleaved Sentences. A sub-sample of our participants (n = 45) was re-assessed three weeks following the initial testing in all the tasks that required a speeded response in order to calculate test-retest reliability. 4. Results Table 1 shows the descriptive statistics for all the tasks used as well as the correlations between the measures. Before running the correlational and regression analyses we log transformed the scores in all three measures of operation planning because they were positively skewed. The results of the correlational analysis indicated first that Planned Connections, Planned Patterns, and Planned Codes correlated moderately with each other (rs ranged from 0.38 to 0.47) and weakly with CTC (rs ranged from −0.23 to −0.29). Second, Planned Connection and CTC correlated with Nelson-Denny Reading Test and Planned Connections, Planned Patterns, and Planned Codes with Interleaved Sentences. Next, we performed a principal components analysis with varimax rotation to examine the factorability of our Planning tasks. In line with our expectation, Planned Connections, Planned Patterns, and Planned Codes loaded on one factor (factor loadings N 0.801) and CTC on another (factor loading = 0.931). The two factors together accounted for 68% of the variance and correlated 0.30 with each other. To examine the contribution of the two levels of Planning to NelsonDenny Reading Test and Interleaved Sentences, we performed hierarchical regression analysis. Vocabulary and reading speed were entered in the regression equation as a block at step 1. Operation planning (factor score derived from Planned Connections, Planned Codes, and Planned Patterns) and action planning (factor score derived from CTC) were entered in the regression equation interchangeably at step
Please cite this article as: Das, J.P., & Georgiou, G.K., Levels of planning predict different reading comprehension outcomes, Learning and Individual Differences (2016), http://dx.doi.org/10.1016/j.lindif.2016.04.004
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Table 1 Descriptive statistics and correlations between all the measures. 1.
2. 0.47⁎⁎
1. Planned connections 2. Planned patterns 3. Planned codes 4. CTC 5. Text reading speeda 6. Vocabulary 7. Nelson-Denny 8. Interleaved Sentencesa Mean SD
92.57 24.59
86.74 29.15
3.
4.
5.
6.
7.
8.
0.38⁎⁎ 0.42⁎⁎
−0.29⁎⁎ −0.23⁎ −0.25⁎
−0.34⁎⁎ −0.31⁎⁎ 0.15⁎ 0.22
−0.14 −0.17 −0.17 0.31⁎⁎ 0.15
−0.35⁎⁎ −0.22 −0.24 0.39⁎⁎ 0.36⁎⁎ 0.65⁎⁎
0.25⁎ 0.30⁎⁎ 0.38⁎⁎ −0.22 −0.31⁎⁎ −0.16 −0.35⁎⁎
205.88 56.13
204.79 10.29
0.80 0.12
3.89 1.75
29.40 5.43
102.03 43.31
Note. CTC = Crack the Code; WRE = Word Reading Efficiency. ⁎ p b 0.05. ⁎⁎ p b 0.01. a Measured in seconds.
2. Significance levels, standardized beta coefficient, and R2 changes are reported in Table 2. After controlling for vocabulary and reading speed, operation planning predicted significantly only Interleaved Sentences and accounted for 6% of unique variance. In contrast, action planning predicted significantly only Nelson-Denny Reading Test and accounted for 8% of unique variance. 5. Discussion In the present study, we first sought to examine if different Planning tasks (some of which were selected from the D-N CAS and some of which were new) could be used to operationalize action and operation planning. The conceptual framework of action and operation planning was reasonably supported by our findings. The results of factor analysis showed that Planned Connections, Planned Patterns, and Planned Codes were loading on one factor and CTC on a second factor. This extends earlier findings by Parrila et al. (1996) who used children from grades 3, 5, 7, 9, and 11, and a different set of operation planning tasks (Matching Numbers and Planned Search). A second objective of this study was to examine if the two levels of Planning could predict reading comprehension, after controlling for the effects of two key predictors of comprehension (vocabulary and reading speed). As shown in the results of the regression analyses, Planning not only predicted performance on tests of comprehension, but also went a step further — suggesting a differential prediction derived from the conceptual division of Planning tests. The association of CTC with the Nelson-Denny comprehension test suggests that the latter behaves like a problem-solving task, essentially similar to a test that engages reasoning and strategies. After all, Nelson-Denny Reading Test measures comprehension by asking questions about the text that involve higher level abilities. These abilities comprise activating relevant background information, generating inferences while reading, and combining information in working memory to form a mental
Table 2 Results of hierarchical regression analysis with Nelson Denny reading test and interleaved sentences as outcome measures. Nelson-Denny
Interleaved sentences
Step
Predictors
β
ΔR2
β
ΔR2
1. 1. 1.
Action planning Operation planning Vocabulary Reading speed Action planning Operation planning
0.390⁎⁎⁎ −0.248⁎⁎ 0.609⁎⁎⁎ 0.270⁎⁎⁎ 0.280⁎⁎ −0.143
0.15⁎⁎⁎ 0.07⁎⁎ 0.49⁎⁎⁎
−0.221⁎ 0.319⁎⁎⁎ −0.013 −0.308⁎⁎⁎ −0.189 0.255⁎
0.05⁎ 0.10⁎⁎ 0.10⁎⁎
2. 2.
⁎ p b 0.05. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.
0.08⁎⁎ 0.02
0.03 0.06⁎
representation of text (Perfetti, Landi, & Oakhill, 2005). It can be argued here that the approach used to solve problems in CTC is highly similar to the above actions. In contrast, Interleaved Sentences involve search and find operations. Each test item presented four jumbled sequences – their common connection needs to be searched and their logical order needs to be found – that resembled planning operations (Das & Misra, 2015). A similar contribution of operation planning was reported recently by Kendeou et al. (2015). In their study, comprehension was operationalized with the CBM-Maze test that requires children to choose the missing word from several alternatives during reading of age appropriate texts in a limited time. Taken together, these findings suggest that the relationship between Planning (and by extension of EF) and reading comprehension depends on the type of Planning and reading comprehension tasks used. Some limitations of the present study are worth mentioning. First, we did not include a measure of activity planning. This is particularly difficult as it takes several years before individuals can realize their life goals. Second, we only included a single measure of action planning. Although we wanted to have a second measure of action planning, these tasks take long to administer (see e.g., Tower tasks) and we could only assess our participants for about an hour. Certainly, future studies should include more tasks like CTC that measure action planning. Finally, we administered Planned Patterns as a third measure of operation planning (along with Planned Connections and Planned Codes from D-N CAS) instead of Matching Numbers from D-N CAS. Planned Connections and Planned Patterns are instances of extradimensional shift (shift from sequence of alphabets to sequence of numerals in the first; shift from a ‘Z’ pattern to an ‘N’ pattern in the second). In Luria's (1966) procedure, a set shift is instantiated in both parts of the two tasks; however in Planned Patterns, reciprocal semantic response is the critical operation. Luria has used a reciprocal motor response as an alternative form—namely “when I do this (show my closed fist), you respond like that (show your open palm) and vice versa. Both procedures require an intense activity of the prefrontal cortex. The cognitive load in semantic reversal is greater than the set shift in the first part of Planned Patterns. Obviously, this task compared to the Matching Numbers that we did not include has a theoretical advantage as well as practical implications for the operationalization of EF that involve prefrontal cortex. To conclude, the results reported in this short paper are suggestive, but await a more comprehensive investigation that further elaborates on the use of Leontjev's (1978) conceptual framework in categorizing Planning tests and delineates the core components of various tests of reading comprehension within the broad concept of cognitive planning. Admittedly, the study is not a proof for Leontjev's theory; rather it suggests that the theory is useful in understanding comprehension at easy and difficult levels. Our findings have also shown that putting all Planning tasks in the same basket does not do justice to the complex nature of their relationship with reading comprehension. However,
Please cite this article as: Das, J.P., & Georgiou, G.K., Levels of planning predict different reading comprehension outcomes, Learning and Individual Differences (2016), http://dx.doi.org/10.1016/j.lindif.2016.04.004
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Please cite this article as: Das, J.P., & Georgiou, G.K., Levels of planning predict different reading comprehension outcomes, Learning and Individual Differences (2016), http://dx.doi.org/10.1016/j.lindif.2016.04.004
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Learning and Individual Differences journal homepage: www.elsevier.com/locate/lindif
Levels of planning predict different reading comprehension outcomes J.P. Das, George K. Georgiou ⁎ University of Alberta, Edmonton, Canada
a r t i c l e
i n f o
Article history: Received 28 July 2015 Received in revised form 2 January 2016 Accepted 2 April 2016 Available online xxxx Keywords: Planning Reading comprehension Activity theory Crack the Code Adults
a b s t r a c t The purpose of this study was to examine if different measures of Planning could be used to operationalize action and operation planning, and if these two levels of Planning could predict different reading comprehension outcomes after controlling for vocabulary and reading speed. One hundred eighty-six university students (120 females; mean age = 22.26 years) were assessed on Planning (Planned Connections, Planned Patterns, Planned Codes, and Crack the Code), vocabulary, reading speed, and reading comprehension (Nelson-Denny Reading Test and Interleaved Sentences). The results of factor analysis indicated first that the four Planning tasks were loading on two separate factors conceptually dividing them into two levels: action planning (CTC) and operation planning (Planned Connections, Planned Patterns, and Planned Codes). Second, the results of regression analyses showed that action planning was accounting for unique variance only when predicting performance demanding a higher level of comprehension (Nelson Denny), whereas operation planning predicted performance only in a task requiring a relatively lower level of comprehension (Interleaved Sentences). These findings suggest that the division of Planning into its different levels can help us better understand its contribution to reading comprehension. At the same time, the cognitive demands exerted by different comprehension outcomes may also determine the strength of their relationship with Planning. © 2016 Elsevier Inc. All rights reserved.
1. Introduction Cognitive processes such as Planning are highly relevant in our daily life (e.g., Das, Kar, & Parrila, 1996; Luria, 1966) and key predictors of academic success (e.g., Best, Miller, & Naglieri, 2011; Friedman et al., 2014; Naglieri & Rojahn, 2004; Papadopoulos, Parrila, & Kirby, 2015). Planning is defined as “any hierarchical process in the organism that can control the order in which a sequence of operations is to be performed” (Miller, Galanter, & Pribram, 1960; p. 16). According to Das and Misra (2015), it is the plan that controls human information processing and supplies patterns for essential connections between knowledge, evaluation, and action. In this brief report, we aimed to delineate different levels of Planning by providing tests that could operationalize them and by examining how these levels may relate to reading comprehension. Initially, Luria (1966) identified Planning as one of the three functional units in the brain associated with the functioning of the frontal lobe, especially of the prefrontal cortex. The prefrontal cortex plays a central role in forming goals and objectives and then in devising plans of action required to attain these goals (Miller & Cohen, 2001). Following Luria's pioneer work, Das and colleagues (see e.g., Das et al., 1996; Das, Naglieri, & Kirby, 1994; Naglieri & Das, 1987) conceptualized Planning as one of the major cognitive processes in the PASS (Planning, ⁎ Corresponding author at: Department of Educational Psychology, 6-102 Education North, University of Alberta, Edmonton, AB T6G 2G5, Canada. E-mail address: [email protected] (G.K. Georgiou).
Attention, Simultaneous and Successive processing) theory of intelligence. More recently, Planning has been subsumed under the umbrella of Executive Functions (EF) along with inhibition and working memory (e.g., Clark, Pritchard, & Woodward, 2010; Lehto, Juujärvi, Kooistra, & Pulkkinen, 2003). As such, several studies have shown that complex Planning tasks such as Tower of London and Wisconsin Card Sorting can predict reading (e.g., Sesma, Mahone, Levine, Eason, & Cutting, 2009) and mathematics (e.g., Bull, Espy, & Wiebe, 2008). Finally, Best et al. (2011) have attempted to build bridges between EF and PASS theory by proposing that the three measures of Planning in the DasNaglieri Cognitive Assessment System (D-N CAS; Naglieri & Das, 1997) can be used to operationalize “complex” EF. We provide below a nuanced view of levels of planning that conceptually divide tests for the assessment of Planning. 1.1. Levels of planning Three different levels of Planning can be distinguished within a broad framework adopted from activity theory (Leontjev, 1978). These are activity planning, action planning, and operation planning. As proposed by Leontjev (1978), at the level of activity, Planning can be conceptualized as a method of realizing or aiming towards one's general life goals and motives. The function of activity planning is to mediate between a person's life goals and the external world. In turn, action planning is equivalent to problem solving. It aims at achieving a particular goal or solving a particular problem. It involves forming a mental representation of the problem, the constraints on planning, and the
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Please cite this article as: Das, J.P., & Georgiou, G.K., Levels of planning predict different reading comprehension outcomes, Learning and Individual Differences (2016), http://dx.doi.org/10.1016/j.lindif.2016.04.004
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course of action. Action planning is especially important in tasks where finding the solution requires integration of multiple steps into a coherent process and in tasks that allow more than one way to find the answer. The Crack the Code (CTC) task fits nicely this description (Das et al., 1996). CTC is based on the popular Mastermind game and was developed in 1983 by J. P. Das (Das & Heemsbergen, 1983). Since then, it has been used in several studies (Das, Mensink, & Janzen, 1990; Papadopoulos, Panayiotou, Spanoudis, & Natsopoulos, 2005; Parrila, Das, & Dash, 1996; Parrila, Papadopoulos, & Mulcahy, 1997). Importantly, using factor analysis, Parrila et al. (1996) showed that CTC was somewhat distant from other Planning tasks. This allowed us to suggest that CTC is at a level of activity distinct from other planning tasks. In CTC, participants are shown two to five lines of information that contain three to five colored chips that are in a particular order (see Fig. 1). They are also shown a label indicating how many of the colored chips are in their correct place. The participant's task is to integrate information from all of the information lines and place his/ her set of colored chips on the answer line in such an order that all the information lines are true. At the level of operation, Planning is equivalent to strategies and tactics, and consists of working towards the solution of a problem in accordance with task-imposed constraints. Because the goal or end result is often known, operation planning involves forming a representation of the task and conditions, choosing the possible operations to be applied, and then executing these steps. We suggest that the Planning tasks of the D-N CAS (Naglieri & Das, 1997) engage operation planning (see also Parrila et al., 1996, for empirical evidence). For example, in Planned Codes, individuals know that the end goal is to fill in as many empty boxes as possible with a combination of Os and Xs that corresponds to a given letter. Once they create a representation of the problem, they choose the strategy that will help them achieve their goal and follow the same strategy until the completion of the task. 1.2. Planning and reading comprehension To date, only a handful of studies have examined the role of Planning in reading comprehension and they have provided mixed findings (e.g., Best et al., 2011; Kendeou, Papadopoulos, & Spanoudis, 2015; Locascio, Mahone, Eason, & Cutting, 2010; Naglieri & Rojahn, 2004; Sesma et al., 2009). On the one hand, studies that examined the role of PASS processes in reading comprehension have generally shown that Planning is not a unique predictor of comprehension, when viewed in conjunction with Attention, Simultaneous, and Successive processes (e.g., Georgiou & Das, 2014; Naglieri & Rojahn, 2004; however see also Kendeou et al., 2015). On the other hand, studies investigating the role of Planning (as part of EF) have typically reported significant effects
on reading comprehension (e.g., Cutting, Materek, Cole, Levine, & Mahone, 2009; Latzman, Elkovitch, Young, & Clark, 2010; Locascio et al., 2010; Sesma et al., 2009). For example, Sesma et al. (2009) found that 9- to 15-year-old poor comprehenders had significant deficits in planning and working memory, even when the effects of word-reading skill and vocabulary were controlled for. In Sesma and colleagues' study, Planning was assessed with the Tower of London task. A possible explanation for these conflicting findings could be sought in the different levels of Planning. When operation planning tasks are used (as in D-N CAS), Planning exerts a limited role in reading comprehension. In contrast, when action planning tasks are used, Planning seems to predict reading comprehension. However, an alternative explanation could be sought to the kind of reading comprehension outcomes used. Recent findings suggest that different comprehension tests have different cognitive processing demands (e.g., Kendeou, Papadopoulos, & Spanoudis, 2012; Papadopoulos, Kendeou, & Shiakalli, 2014). Most studies on PASS processes and reading comprehension operationalized the latter with Woodcock-Johnson Passage Comprehension (WJPC). WJPC has been found to be strongly dependent on decoding (Keenan, Betjemann, & Olson, 2008) and working memory (Kendeou et al., 2012). In contrast, Sesma et al. (2009) used the Wechsler Individual Achievement Test-II reading comprehension task that requires not only decoding, but also inference generation and vocabulary knowledge. 2. The present study The purpose of this brief report was to examine if old (Planned Connections, Planned Codes) and newer (Planned Patterns and CTC) Planning tasks can be used to operationalize two different levels of Planning (action planning and operation planning) following Leontjev's (1978) conceptualization and whether the two levels of Planning can predict different reading comprehension measures, after controlling for the effects of vocabulary and reading speed. We hypothesized that Planned Connections, Planned Patterns, and Planned Codes will operationalize operation planning and CTC will operationalize action planning. In addition, we hypothesized that comprehension at a higher level (assessed with Nelson-Denny Reading Test, see below for details) would be like a problem solving task and would involve more of action planning. In contrast, comprehension at a lower level (assessed with Interleaved Sentences, see below for details), would involve more of operation planning. 3. Method 3.1. Participants One hundred eighty-six undergraduate students (120 females and 66 males; Mean age = 22.26 years, SD = 4.07) from the University of Alberta (Canada) participated in the study. The participants were Caucasian and reported English as their native language. All students registered full-time in their studies and received credit in an introductory educational psychology course for their participation. Students who were serviced by Specialized Support and Disabilities Services for severe disabilities were excluded from the study. Written consent was obtained prior to testing. 3.2. Measures
Fig. 1. Item 5 from Crack the Code task.
3.2.1. Action planning Crack the Code (CTC; Das & Heemsbergen, 1983; see also Das & Misra, 2015, for the items) was used to assess action planning. CTC requires participants to determine what the correct sequence of colored chips is when limited amount of information is provided in the instruction lines (see the example in the Introduction). The version used in this study consisted of six items. The six items can be divided into three pairs
Please cite this article as: Das, J.P., & Georgiou, G.K., Levels of planning predict different reading comprehension outcomes, Learning and Individual Differences (2016), http://dx.doi.org/10.1016/j.lindif.2016.04.004
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of formally similar, but progressively more difficult items. In the first two items, two information lines and three colored disks (blue, black, and white) were used. Items 3 and 4 consisted of four disks (blue, black, white, and yellow) and four information lines, and Items 5 and 6 had four disks and three information lines. Despite having fewer information lines, Items 5 and 6 are more difficult because they have fewer matching placements in different information lines, and thus, less readily detectable constraints for the planned moves. A discontinuation rule of two consecutive errors was applied. The experimenter recorded the accuracy of each item. Cronbach's alpha reliability coefficient in our sample was 0.78. 3.2.2. Operation planning Three measures of operation planning were administered: Planned Connections, Planned Codes, and Planned Patterns. The first two tasks were adopted from the D-N CAS (Naglieri & Das, 1997) and have been used in previous studies showing good reliability and validity evidence (e.g., Das et al., 2007; Naglieri & Rojahn, 2004). In Planned Connections, participants were asked to connect sequential stimuli. In Items 1 and 2, participants were asked to connect numbers (1 to 25) that were semirandomly arranged on a page. In Item 3, participants were asked to connect 25 numbers (1–25) and 25 letters (A–Z) in successive order (1, A, 2, B, 3, C). The participant's score was the total time to complete all three items. Test-retest reliability coefficient in a sub-sample of our participants (n = 45) was 0.83. In Planned Codes, participants were asked to fill in as quickly as possible, and in any manner they would choose, empty boxes with a combination of Os and Xs that corresponded to a letter (e.g., A = OX; B = XX; C = OO; D = XO) that was printed on top of each empty box. The task contained two pages, each with a distinct set of codes. At the top of each page there was a legend, which was indicating the combination of Os and Xs that corresponded to each letter. The participants were allowed 1 min to fill in as many empty boxes as possible. The time and number correct for each page was recorded and the participants' score was calculated by first dividing the number correct in each page by the time and then by averaging the scores of the two pages. Test-retest reliability coefficient in a sub-sample of our participants (n = 45) was 0.87. Finally, in Planned Patterns (see Das & Misra, 2015, for the items), the participants were shown pages with boxes arranged in four rows and four columns. The boxes contained numbers in each of their corners that were creating a pattern (N or Z) if viewed in ascending order (from smaller to larger). In the first two items, the participants were asked to say N or Z depending on the pattern that was created by the numbers. In the third item, the participants were asked to say N every time they saw Z and Z every time they saw N. The time to name each page of boxes was recorded and the participant's score was the total time to complete all three pages. The test is a Luria-type test of Planning (Luria, 1966) similar in principle to verbal regulation of reciprocal response: ‘When I show my palm, you respond by showing me your fist; when I show my fist, you show me your palm’. Test-retest reliability coefficient in a subsample of our participants (n = 45) was 0.80. 3.2.3. Vocabulary Peabody Picture Vocabulary Test (PPVT-IV; Dunn & Dunn, 2007) was used to assess receptive vocabulary. In this task, the examiner presented a series of pictures to each participant. There were four pictures on each page and each picture was numbered. The examiner was saying a word describing one of the pictures and asked the participants to point to or say the number of the picture that the word described. The task was discontinued after 10 errors in a set of 12 items. The participant's score was the total number of correct items. Cronbach's alpha reliability coefficient in our sample was 0.90. 3.2.4. Reading speed Text reading speed was assessed with the Nelson-Denny Reading Test (Brown, Fishco, & Hanna, 1993). The participants were asked to
3
read silently the first passage in the test and put a mark to the right of the line they were reading after one minute. The minimum reading rate score in the task is 007 and the maximum 601. Test-retest reliability coefficient in a sub-sample of our participants (n = 45) was 0.81. 3.2.5. Reading comprehension Reading comprehension was assessed with two measures: NelsonDenny Reading Test (Brown et al., 1993) and Interleaved Sentences (Das & Misra, 2015). In Nelson-Denny Reading test, participants were asked to read each one of the seven passages in the test and answer the multiple choice questions following each passage. The test contained 38 multiple-choice questions, each with five answer choices. The time limit to complete the test was 20 min. A participant's score was the total number of correct answers. Cronbach's alpha reliability coefficient in our sample was 0.92. Interleaved Sentences essentially involved reconstructing narration of a story (see Das & Misra, 2015). Participants were presented with a set of four sentences and were asked to read the sentences, put a slash in between sentences, and rank order them so that the passage would make sense (e.g., one night a bear came to the house/a brother and a sister live near the woods/they got really scared and hide under the bed/the sister likes to bake apple pies and leaves them on the front stairs to cool down/). Prior to timed testing, participants were given two practice items to ensure they understood the instructions. The test consisted of six items. Because the sentences were relatively easy, accuracy was high (96%) and was not considered further. The participant's score was the average time to complete all six items. Test-retest reliability coefficient in a sub-sample of our participants (n = 45) was 0.85. 3.3. Procedures All tests were individually administered by two graduate students who received extensive training on test administration. Testing lasted approximately an hour and took place in a quiet room at the university. The tasks were administered in the following order: Nelson-Denny Reading Test, Planned Connections, Planned Patterns, Planned Codes, Crack the Code, Vocabulary, Text Reading Speed, and Interleaved Sentences. A sub-sample of our participants (n = 45) was re-assessed three weeks following the initial testing in all the tasks that required a speeded response in order to calculate test-retest reliability. 4. Results Table 1 shows the descriptive statistics for all the tasks used as well as the correlations between the measures. Before running the correlational and regression analyses we log transformed the scores in all three measures of operation planning because they were positively skewed. The results of the correlational analysis indicated first that Planned Connections, Planned Patterns, and Planned Codes correlated moderately with each other (rs ranged from 0.38 to 0.47) and weakly with CTC (rs ranged from −0.23 to −0.29). Second, Planned Connection and CTC correlated with Nelson-Denny Reading Test and Planned Connections, Planned Patterns, and Planned Codes with Interleaved Sentences. Next, we performed a principal components analysis with varimax rotation to examine the factorability of our Planning tasks. In line with our expectation, Planned Connections, Planned Patterns, and Planned Codes loaded on one factor (factor loadings N 0.801) and CTC on another (factor loading = 0.931). The two factors together accounted for 68% of the variance and correlated 0.30 with each other. To examine the contribution of the two levels of Planning to NelsonDenny Reading Test and Interleaved Sentences, we performed hierarchical regression analysis. Vocabulary and reading speed were entered in the regression equation as a block at step 1. Operation planning (factor score derived from Planned Connections, Planned Codes, and Planned Patterns) and action planning (factor score derived from CTC) were entered in the regression equation interchangeably at step
Please cite this article as: Das, J.P., & Georgiou, G.K., Levels of planning predict different reading comprehension outcomes, Learning and Individual Differences (2016), http://dx.doi.org/10.1016/j.lindif.2016.04.004
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Table 1 Descriptive statistics and correlations between all the measures. 1.
2. 0.47⁎⁎
1. Planned connections 2. Planned patterns 3. Planned codes 4. CTC 5. Text reading speeda 6. Vocabulary 7. Nelson-Denny 8. Interleaved Sentencesa Mean SD
92.57 24.59
86.74 29.15
3.
4.
5.
6.
7.
8.
0.38⁎⁎ 0.42⁎⁎
−0.29⁎⁎ −0.23⁎ −0.25⁎
−0.34⁎⁎ −0.31⁎⁎ 0.15⁎ 0.22
−0.14 −0.17 −0.17 0.31⁎⁎ 0.15
−0.35⁎⁎ −0.22 −0.24 0.39⁎⁎ 0.36⁎⁎ 0.65⁎⁎
0.25⁎ 0.30⁎⁎ 0.38⁎⁎ −0.22 −0.31⁎⁎ −0.16 −0.35⁎⁎
205.88 56.13
204.79 10.29
0.80 0.12
3.89 1.75
29.40 5.43
102.03 43.31
Note. CTC = Crack the Code; WRE = Word Reading Efficiency. ⁎ p b 0.05. ⁎⁎ p b 0.01. a Measured in seconds.
2. Significance levels, standardized beta coefficient, and R2 changes are reported in Table 2. After controlling for vocabulary and reading speed, operation planning predicted significantly only Interleaved Sentences and accounted for 6% of unique variance. In contrast, action planning predicted significantly only Nelson-Denny Reading Test and accounted for 8% of unique variance. 5. Discussion In the present study, we first sought to examine if different Planning tasks (some of which were selected from the D-N CAS and some of which were new) could be used to operationalize action and operation planning. The conceptual framework of action and operation planning was reasonably supported by our findings. The results of factor analysis showed that Planned Connections, Planned Patterns, and Planned Codes were loading on one factor and CTC on a second factor. This extends earlier findings by Parrila et al. (1996) who used children from grades 3, 5, 7, 9, and 11, and a different set of operation planning tasks (Matching Numbers and Planned Search). A second objective of this study was to examine if the two levels of Planning could predict reading comprehension, after controlling for the effects of two key predictors of comprehension (vocabulary and reading speed). As shown in the results of the regression analyses, Planning not only predicted performance on tests of comprehension, but also went a step further — suggesting a differential prediction derived from the conceptual division of Planning tests. The association of CTC with the Nelson-Denny comprehension test suggests that the latter behaves like a problem-solving task, essentially similar to a test that engages reasoning and strategies. After all, Nelson-Denny Reading Test measures comprehension by asking questions about the text that involve higher level abilities. These abilities comprise activating relevant background information, generating inferences while reading, and combining information in working memory to form a mental
Table 2 Results of hierarchical regression analysis with Nelson Denny reading test and interleaved sentences as outcome measures. Nelson-Denny
Interleaved sentences
Step
Predictors
β
ΔR2
β
ΔR2
1. 1. 1.
Action planning Operation planning Vocabulary Reading speed Action planning Operation planning
0.390⁎⁎⁎ −0.248⁎⁎ 0.609⁎⁎⁎ 0.270⁎⁎⁎ 0.280⁎⁎ −0.143
0.15⁎⁎⁎ 0.07⁎⁎ 0.49⁎⁎⁎
−0.221⁎ 0.319⁎⁎⁎ −0.013 −0.308⁎⁎⁎ −0.189 0.255⁎
0.05⁎ 0.10⁎⁎ 0.10⁎⁎
2. 2.
⁎ p b 0.05. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.
0.08⁎⁎ 0.02
0.03 0.06⁎
representation of text (Perfetti, Landi, & Oakhill, 2005). It can be argued here that the approach used to solve problems in CTC is highly similar to the above actions. In contrast, Interleaved Sentences involve search and find operations. Each test item presented four jumbled sequences – their common connection needs to be searched and their logical order needs to be found – that resembled planning operations (Das & Misra, 2015). A similar contribution of operation planning was reported recently by Kendeou et al. (2015). In their study, comprehension was operationalized with the CBM-Maze test that requires children to choose the missing word from several alternatives during reading of age appropriate texts in a limited time. Taken together, these findings suggest that the relationship between Planning (and by extension of EF) and reading comprehension depends on the type of Planning and reading comprehension tasks used. Some limitations of the present study are worth mentioning. First, we did not include a measure of activity planning. This is particularly difficult as it takes several years before individuals can realize their life goals. Second, we only included a single measure of action planning. Although we wanted to have a second measure of action planning, these tasks take long to administer (see e.g., Tower tasks) and we could only assess our participants for about an hour. Certainly, future studies should include more tasks like CTC that measure action planning. Finally, we administered Planned Patterns as a third measure of operation planning (along with Planned Connections and Planned Codes from D-N CAS) instead of Matching Numbers from D-N CAS. Planned Connections and Planned Patterns are instances of extradimensional shift (shift from sequence of alphabets to sequence of numerals in the first; shift from a ‘Z’ pattern to an ‘N’ pattern in the second). In Luria's (1966) procedure, a set shift is instantiated in both parts of the two tasks; however in Planned Patterns, reciprocal semantic response is the critical operation. Luria has used a reciprocal motor response as an alternative form—namely “when I do this (show my closed fist), you respond like that (show your open palm) and vice versa. Both procedures require an intense activity of the prefrontal cortex. The cognitive load in semantic reversal is greater than the set shift in the first part of Planned Patterns. Obviously, this task compared to the Matching Numbers that we did not include has a theoretical advantage as well as practical implications for the operationalization of EF that involve prefrontal cortex. To conclude, the results reported in this short paper are suggestive, but await a more comprehensive investigation that further elaborates on the use of Leontjev's (1978) conceptual framework in categorizing Planning tests and delineates the core components of various tests of reading comprehension within the broad concept of cognitive planning. Admittedly, the study is not a proof for Leontjev's theory; rather it suggests that the theory is useful in understanding comprehension at easy and difficult levels. Our findings have also shown that putting all Planning tasks in the same basket does not do justice to the complex nature of their relationship with reading comprehension. However,
Please cite this article as: Das, J.P., & Georgiou, G.K., Levels of planning predict different reading comprehension outcomes, Learning and Individual Differences (2016), http://dx.doi.org/10.1016/j.lindif.2016.04.004
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Please cite this article as: Das, J.P., & Georgiou, G.K., Levels of planning predict different reading comprehension outcomes, Learning and Individual Differences (2016), http://dx.doi.org/10.1016/j.lindif.2016.04.004