The study used the model of hierarchical complexity (MHC) to test the theory that different domains in development would develop in synchrony, allowing an individual to solve tasks from various domains using the same mental structure for each task. The MHC instruments used were the empathy, helper person, counselor patient, breakup, caregiver, algebra, balance beam, infinity and laundry instruments. The instruments can be categorized as belonging to two different subdomains, the social subdomain, and the logic/mathematics/physical sciences subdomain. Instruments in this social subdomain measure developmental stage in a variety of social contexts. These social contexts included empathy for person after an accident, guidance and assistance by a helper, counseling patients, understanding romantic breakups, and caring for children and infants. The other subdomain is composed of mathematical (algebra & infinity), logical (laundry), and physical science (balance beam). In order to conclude how related the performances were, three analyses were carried out.First, Rasch analysis yielded person scores akin to person stage scores. Second, regression analysis was conducted to assess how well the order of hierarchical complexity (OHC) of the items predicted the Rasch difficulty of the items. Third a principal axis factoring was carried out with the person Rasch scores for every instrument. Irrespective of domains, if each instrument loaded on the first factor with all the factor scores over .7 and if the first factor accounted for more than 70% of the variance, then that would show that all instruments were part of a single domain. In each case the MHC accounted for a large amount of variance with r values over .7.The principal axis factoring showed that person scores on each instrument loaded on the first factor (90.51% of the variance). All the factor scores on the first factor were over .85. There were very low loadings only on the second factor (4.947% of the variance). This implies that the instruments from the social subdomain and instruments from the logic/mathematics/physical sciences belong to a single domain.

A number of different previous methods for measuring "smarts" have led to the model of hierarchical complexity (MHC), a context free neo-Piagetian mathematical model of behavioral complexity. It provides a way to classify tasks as to their hierarchical complexity. Using the model of hierarchical complexity, this study examines how differences in rate of stage change results in a difference in the highest average stage (smarts") attained by 70 year old adults. The average stage of development ("smarts") was shown to be predicted by the log of age with an r = .79. It uses data from Colby, Kohlberg, Gibbs, Lieberman (1983) to test the model. It also predicts that on the average there is one stage of development during adulthood.

Thirty-nine nonliterate Nepalese adults were given 2 stage-based isolation-of-variables instruments: the thatched roof problem and laundry problem. The thatched roof problem was very similar to the laundry instrument, just differing in content. The thatched roof task was used as the training instrument and the laundry instrument was used as the transfer task instrument. The participants practiced on the thatched roof instrument. With the transfer task instrument, correct answers were reinforced with money. From the beginning of the measured stage of performance in the transfer instrument, the M stage 9.77 (SD ϭ 1.48) increased to M stage 10.72 (SD ϭ 1.45) at the end of the transfer task training and testing, t(38) ϭ 16.7013, p ϭ .00000. This is roughly 1 stage from pretest to posttest. Also, the frequency of people performing at the lower stages (Primary Stage 8 and Concrete Stage 9) decreased at posttest. The frequency of people at the higher stages (Abstract Stage 10 to Metasystematic Stage 13) increased at posttest. This showed that training with reinforcement had a positive effect on increasing stage performance from pretest to posttest. This finding strongly suggests that all testing should include repeated presentation of very similar items and that reinforcement needs to be used for correct answers. Otherwise there is the risk of underestimating what tasks people can successfully complete and what their stage of performance is.

This article proposes and explores the kinds of computational thinking, creative practices, design activities, and inclusive learning opportunities provided to diverse high school youth when designing integrated systems through simultaneously physically and digitally responsive wearable games and systems. Previous work in this area, conducted by Richard, coined the term “bidirectionally responsive design” (BRD) to describe the design of dual-feedback systems using multiple digital and physical interfaces. BRD also emphasizes using simplified fabrication tools, media and coding platforms, and microcontrollers common in youth content creation communities and makerspaces. This study provides a framework to analyze computational concepts, practices, and perspectives that leverage an integrated systems and multimodal learning approach, such as BRD, adding to, building on, and integrating previous analytic approaches to looking at Scratch coding, media design, physical computing and e-textiles. Using a detailed case study of one team during one of the early workshop iterations, we conduct a multimodal analysis of bidirectionally responsive making activities and discuss the ways that they present novel understanding of integrating diverse interests and encouraging collaborative and distributed computational thinking. We further examine how BRD operationalizes and extends multimodal learning theory by adding tangible and integrative dimensions as additional modalities learners can leverage to facilitate meaning making, metacognition, and agency. We also discuss how designing integrated systems, as facilitated through BRD, provides an opportunity to engage in authentic practices around the design of complex systems.

A new conceptual account of operant conditioning based on coordinating 3 procedural steps of respondent conditioning processes is introduced. In this account, stimuli, actions and conditioning are only used procedurally and conceptually. Convergence of 2 theories is used to support this account: (1) the model of hierarchical complexity and (2) ordering of evolutionary development and the corresponding changes in neural structure and biochemistry of organisms. Three very different cases of procedural respondent conditioning are used. The only commonality among the 3 respondent conditioning steps is the basic procedure. Those procedural steps are the “what to do” (Step 1), “when to do” (Step 2), and “why to do” (Step 3). In Step 1 of the respondent conditioning the representation of behavior takes on the elective properties of the SR+ making the representation of behavior salient. We leave the representation of behavior undefined. One might use common notions of it instead. In Step 2, the now salient representation of behavior (rb) is paired with an environmental S. This makes the S elicit the representation of a behavior which requires the saliency of the representation of a behavior. In Step 3, the environmental S is paired with the SR+ making the S more salient and valuable. When the environmental stimulus is more salient, the representation of a behavior rate relative to other representation of a behavior’s not associated with reinforcement increases.