Learners with ADHD lack more than “normal” learners the ability to adequately self-regulate their learning processes (Harris, 2004; Barkley, 2005), a competence which is essential for the effective execution of the majority of computer-based learning activities (Koerndle et al., 2006). With regard to the expected benefits of instructional technology for this learner-group and the increasing importance of lifelong learning the general question occurs, under which circumstances and to what degree learners with ADHD are able and willing to use computers and internet for self-regulated learning.

The typical symptoms of learners with ADHD (high distractibility, low frustration tolerance, problems to persistently strive for goals; Barkley, 2005) give raise to the assumption, that especially the motivational self-regulation of learning (Pintrich, 2000; Rheinberg, 2000; de Corte, 2005) might be maladaptive. There is further evidence, that among the several variables involved especially area-specific self-efficacy expectations play an important role in the regulation process and are expected to be significant predictors for the willingness of utilizing computers for learning purposes and the efficiency of the actual learning process (Bandura, 1994).

In the study presented (N=40) we tried to identify possible relations between the extent of attention deficits and further important learner-related antecedents of motivational self-regulation (especially area-specific self-efficacy), its relations to learner’s motivational/ functional states in the context of a nonlinear computer-based learning task and the utilization of computers in the normal course of life.

Background:

According to the results of several studies (Xu et al., 2002, Solomonidou et al., 2004) the utilization of computers and internet for learning purposes seems to have positive impacts on motivation, attention and academic performance of learners with ADHD. It was shown though, that various characteristics and preferences of this learner group have to be considered to design efficient computer-based learning scenarios. Amongst others learners with ADHD lack (even more than “normal” learners) the ability to adequately self-regulate their learning process (Harris et al., 2004; Barkley, 2005), a competence which is essential for the effective execution of the majority of computer-based learning activities (Koerndle et al., 2006). Against this background and with regard to the increasing importance of lifelong learning the general question occurs, under which circumstances and to what degree learners with ADHD are able and willing to use computers and internet for self-regulated learning.

Self-regulated learning is a complex process taking place in different psychic areas and domains. Pintrich’s framework (2000) distinguishes between 4 phases (planning/ activation, monitoring, control, reaction/ reflection) and 4 areas (cognition, motivation, behavior, context). To be able to compensate the mentioned deficits of learners with ADHD (i.e. by providing appropriate self-regulation trainings or designing computer-based learning environments supporting the regulation process) it is essential to know (but not examined so far), deficits in what phases and areas are responsible for the poor execution of self-regulating processes.

The typical symptoms of learners with ADHD (such as high distractibility, low frustration tolerance, problems to persistently strive for goals; Barkley, 2005) give raise to the assumption, that mainly the motivational self-regulation of learning might be maladaptive, a component of self-regulation, which de Corte (2005) referred to as metamotivation. Rheinberg (2000) presented a cluster of antecedents (person vs. situation), aspects of current motivation and mediating variables (such as motivational/ functional state of the learner) which has to be considered to understand the dynamics of learning motivation and its effects on self-regulated learning and thereby provided an appropriate framework for our research. There is further evidence, that among the mentioned variables especially area-specific self-efficacy expectations play an important role in the regulation process and are expected to be significant predictors for the willingness of utilizing computers for learning purposes and the efficiency of the actual learning process (Bandura, 1994).

Research Questions and Hypotheses:

Based on this theoretical background in general and Rheinberg’s framework (2000) in particular the subsequently described study tried to identify possible relations between the extent of attention deficits and further important learner-related antecedents of the motivational self-regulation (especially area-specific self-efficacy), its relations to motivational/ functional states of learners in the context of nonlinear computer-based learning tasks and the utilization of computers in the normal course of life. We expected that higher attention deficits would come along with lower self-efficacy, which in turn should be related to the motivational states during the learning process as well as the extent and nature of utilizing computers in everyday life. Furthermore we hypothesized relations between attention deficits and the functional states during the learning process.

Study:

40 trainees (aged 16-20 years) attending either regular or special vocational schools took part in our study. In order to provide them with a computer-based learning task not being related to their specific vocational background and requiring self-regulated learning we designed a nonlinear hypertext-based learning environment with total learner control dealing with the subject “Health Care Reform in Germany”. Their task was to find and learn as much as possible about laws and regulations relevant to their personal context.

Before the actual learning episode our test persons were presented with a selection of questionnaires measuring attention deficits (Adult ADHD Self-Report Scale, WHO), self-efficacy for computer- and web-based learning (Domes, 2004), self-regulatory knowledge, strategies and control beliefs (MSLQ, Pintrich, 1991; AVSI, McCann & Garcia, 1999), demographic information, the utilization of computers in everyday’s life and subject- and computer-related previous knowledge. After the introduction into the learning environment learners had to fill in a questionnaire dealing with concrete goals/ expectations and the QCM (Questionnaire for Current Motivation, Vollmeyer & Rheinberg, 1998). During the learning episode the participants were frequently asked to complete a short questionnaire measuring their current motivation (motivational state) and flow (functional state). For a more detailed analysis of their self-regulating process all computer-related actions were logged and video-taped (not yet analysed). After the learning session the learners were presented with a short post test measuring learning success. To establish a sufficient level of learning and test motivation (besides their intrinsic motivation because of personal relevance) we incorporated and announced a financial reward based on the results of the post test.

Findings and Discussion:

As a preliminary result of our analysis we found the expected significant relationships between attention deficits and self-efficacy for computer- and web-based learning (primarily aspects of monitoring and control) as well as between attention deficits and motivational/ functional states during the learning process, whereas only insignificant relations between self-efficacy and motivational states occurred. Furthermore we found significant relations between self-efficacy (primarily technological-methodical aspects) and the extent and nature of utilizing of computers in everyday’s life, but not between the latter and attention deficits.