students' conceptions of learning, the classroom environment, and approaches to learning

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Students' Conceptions of Learning, the ClassroomEnvironment, and Approaches to LearningBarry C. Dart a , Paul C. Burnett a , Nola Purdie a , Gillian Boulton-Lewis a , Jenny Campbell a &David Smith aa Queensland University of Technology , AustraliaPublished online: 01 Apr 2010.

To cite this article: Barry C. Dart , Paul C. Burnett , Nola Purdie , Gillian Boulton-Lewis , Jenny Campbell & David Smith (2000)Students' Conceptions of Learning, the Classroom Environment, and Approaches to Learning, The Journal of Educational Research,93:4, 262-270, DOI: 10.1080/00220670009598715

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Students’ Conceptions of Learning, the Classroom Environment, and Approaches to BARRY C. DART PAUL C. BURNETT NOLA PURDIE

JENNY CAMPBELL DAVID SMITH

GILLIAN BOULTON-LEWIS

‘ Queensland University of Technology, Australia

ABSTRACT A model that hypothesized relationships between high school students’ conceptions of learning, their perceptions of the classroom environment, and their approaches to learning was tested using structural equation modeling. Results suggested that important associations exist between conceptions of learning and approaches to learning. Students who reported qualitative and experiential conceptions were likely to use deep approaches to learning, whereas students who had quantitative conceptions of learning tended to use surface approaches. The implications of these findings for teachers and the way they function in the classroom environment are discussed.

he influences of students’ conceptions of learning and T their perceptions of the classroom learning environment on their approaches to learning are examined in this study. We focused on the first two ps (presage and process) of Biggs’s (1993) 3P model by testing the hypothesis that students’ conceptions of learning do influence how they perceive their classroom learning environment and how they engage in learning.

The 3P Model of Learning

Biggs (1993) proposed a framework for understanding student learning through consideration of the relations between what teachers and students do and think and the nature of student learning outcomes. That model, common- ly referred to as the 3P model (presage, process, and product factors), represents not only a linear movement from presage to process to product, but also allows for interactions between the components that form an integrated system, which is in equilibrium. A change to any part of the system affects other parts of the system (see Figure 1).

Learning

Presage factors include both student characteristics and aspects of the teaching context. Student presage factors are relatively stable learning-related characteristics that include conceptions of learning, prior knowledge, motiva- tion, work habits, study skills, abilities; locus of control orientation, perceived self efficacy, learning style, and social and cultural factors. Teaching presage factors include conceptions of learning and teaching, teaching style and methods, cumculum organization, task difficulty, assessment procedures; time available, freedom allowed, classroom management, resource materials, and the classroom climate.

Process factors are the result of the interaction between student and teaching presage factors and refer to the way students handle the learning task by adopting deep, surface, or achieving approaches to learning. Deep is defined as a learning approach characterized by an intention to seek meaning of the material being studied by using the material to elaborate and transform it. In the surface approach, the material being studied is reproduced using routine procedures. A deep approach to learning is associated with constructivist teaching (Biggs & Moore, 1993; Dart, 1997; Tang, 1998), which suggests that learners actively construct knowledge for themselves. On the other hand, a surface approach to learning (Biggs & Moore, 1993; Dart, 1997; Tang, 1998) is related to the traditional transmission model of teaching in which information is transferred from teachers to learners and in which learners assume passive roles. (In this study, we did not focus on an achieving approach, in which the intention is ego enhancement or excelling in organized activities and cue-seeking behavior.)

Address correspondence to Paul C. Burnett, Charles Sturt Uni- versity, Faculty of Education, School of Teacher Education, Panorama Avenue, Bathurst NSW 2795, Australia.

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~ ~~~

Figure 1. The 3P Model as a Classroom System (Biggs 81 Moore. 1993, p. 451).

Presage Process Product -

Feedback: efficacy beliefs, attributions for success, failure

Student characteristics Conceptions of learning Developmental factors Social factors Abilities E.xpectations of success

Preferred approach to

\ and failure

learning

Quantitative Qualitative

Metalearning P- Approaches to given Student t 1 Teacher

perceptions perceptions I 1 deep, achieving

Tcacher: Conceptions of learning and teaching

Institution: Curriculum, method. assessment, rules, procedures

about teaching

Product factors are the outcomes of learning and are de- termined mainly by the approaches to student learning. Out- comes may be categorized quantitatively (how much is learned), qualitatively (how well it is learned), and institu- tionally (relating to either quantitative or qualitative outcomes or both, leading to the awarding of grades).

Personal and Contextual Characteristics

The important influences of the learning context and the personal characteristics of the learners in their approaches to learning have been emphasized in recent research at the tertiary level (Clarke & Dart, 1994; Dart, 1994, 1998; Dart & Clarke, 1991, 1992; Entwistle, 1987; Ramsden, 1992, 1994) and to a lesser extent in secondary school classrooms (Campbell & Smith, 1997; Dart, Burnett, Boulton- Lewis, Campbell & Smith, in press; Ramsden, Martin, & Bowden, 1989). Those researchers identified the following influences: (a) factors in the academic environment that affect approaches to studying such as teaching methods, teachers’ openness to students, freedom in learning assessment methods, clear goals and standards, vocational rele- vance, press for understanding, conceptions of teaching and learning, appropriate workload and (b) students’ personal characteristics such as perceived self-ability, locus of control orientation, conceptions of learning, and interest in and background knowledge of the material to be learned.

Biggs (1993) noted that students may have a preferred orientation toward, or an intention to use, a deep or surface

approach to learning, but it is how they perceive the learning environment that will arouse or inhibit that approach. That perception is dependent on how students interpret the factors present in the learning environment in the light of their personal characteristics. Students’ conceptions of learning and their perceptions of the classroom climate are the personal and teaching presage factors that we examined in this study to determine their effects on students’ approaches to learning.

Conceptions of Learning

Students’ views on learning have increasingly become a focus of interest and research in recent years. Consequent- ly, researchers have found that students’ conceptions of learning are related to their approaches to learning, as well as to the quality of their educational outcomes (Dart, 1998; Marton, 1988; Prosser & Millar, 1989; Van Rossum & Schenk, 1984; Trigwell & Prosser, 1991). Saljo (1979) and Marton, Dall’Alba, and Beaty (1993) developed frameworks in which students’ views on learning were arranged hierarchically. Following are the first five levels of conceptions, which are similar for both frameworks (Marton et al. [ 19931 identified a sixth level, which is an existential exten- sion of the fifth level):

1. Increasing one’s knowledge 2. Memorizing and reproducing 3. Applying 4. Understanding

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264 The Journal of Educational Research

5. Seeing something in a different way 6. Changing as a person

Biggs (1994) proposed that there are two perspectives on learning which he called quantitative and qualitative. The quantitative outlook suggests that learning is about acqui- sition and accumulation of content so that the more you know, the more proficient a learner you are. The qualitative perspective proposes that learning is concerned with understanding and meaning by relating or connecting new material to prior knowledge. Saljo (1979) and Marton et al.’s (1993) frameworks suggest that, in both hierarchies, Levels 1, 2, and 3 can be considered as indicative of a quantitative outlook, and Levels 4, 5 , and 6 as representa- tive of a qualitative outlook. Van Rossum and Schenk (1984) used that reduction of conceptions in two categories: (a) reproductive-Levels 1, 2, and 3 and (b) constructive-Levels 4 and 5. They found that students who used a surface approach to learning held a reproductive conception of learning, whereas those who used a deep approach held a constructive conception.

Purdie and Hattie (1997) developed a scale to measure secondary students’ conceptions of learning. As well as qualitative and quantitative perspectives (among others), there is an experiential perspective, which suggests that learning is the product of daily experiences. Allan (1996) asserted that the critical variable in determining how students learn is their conception of learning in a given context; he referred to Gibbs (1995, p. 23) who stated that “the connection between these underlying conceptions of learning and the approach students take to specific learning tasks is so strong that it is possible to predict the quality of learning outcomes directly from students’ conceptions of learning.”

Classroom Learning Environment

Teachers’ and students’ perceptions of classroom learning environments (both in secondary schools and universi- ties) have received increasing attention from educators (see Fraser, 1991, for an overview of the research). However, there have been few attempts to relate approaches to learning to perceptions of the learning environment of secondary school classrooms. Ramsden et al. (1989) investigated the influence of school environment as perceived by final-year secondary students on their approach to learning. They reported associations between the deep approach and achievement strategy with teaching support, structure and cohesiveness, independence in learning, and preparation for higher education (positive learning climate variables). Sur- face approach and achievement motives were linked to emphasis on formal achievement. In a recent study, Dart et al. (in press) found that a deep approach is related to a classroom learning environment perceived as having high levels of personalization, participation, and investigative learning skills; the three dimensions of the environment are signifi- cantly related to each other.

Clarke and Dart (1994) proposed that a convenient way of integrating elements of models of learning into personal and environmental factors is to classify the elements as cognitive and affective. They provided as an example a learning environment climate that emphasized problem-solving activities and fostered the development of a variety of cognitive skills. Further, a classroom climate can exist in which interpersonal conflicts can be resolved so that students’ feelings of self-worth are developed and retained; that is, climate also can have an affective dimension. Thus, both cognitive and affective dimensions of the classroom environment can influence learning behavior.

In this study, we tested a hypothesized model that students’ conceptions of learning influence how they perceive their classroom learning environment, which, in turn, is related to how they learn. We organized the model in terms of the following seven latent variables:

Personal (conceptions) presage

1. Qualitative conceptions of learning 2. Quantitative conceptions of learning 3. Experiential conceptions of learning

Teaching (contextual) presage

4. Affective personalization 5. Cognitive investigation

Process

6. Deep approach 7. Surface approach

On the basis of previous theory and research, the test model is shown in Figure 2; unidirectional arrows represent paths of influence, and two-way arrows represent relationships. The model does not test directly for interaction effects.

In this model, we hypothesized the following: (a) Level of personalization perceived in the learning environment directly influences deep approaches to learning and investigative skills; (b) perception of the learning environment as encouraging investigative or problem-solving skills in learning directly influences the adoption of deep approaches to learning; (c) use of surface approaches to learning negatively influences deep approaches to learning; (d) personalization and investigation mediate the effects of qualitative conceptions of learning on deep approaches to learning; (e) qualitative conceptions of learning have a negative influence on the use of deep approaches to learning and on surface approaches to learning; (f) experiential conceptions of learning have a negative influence on deep approaches to learning as well as on surface approaches to learning; and (8) quantitative conceptions of learning negatively influence surface approaches to learning as well as deep approaches to learning.

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Method

Participants

Participants in this study included 457 students from 22 classes in two metropolitan Australian secondary schools. Of the 457 students i n Grades 8 through 12, 56.5% were girls and 43.5% were boys. The students completed ques- tionnaires relating to their conceptions of learning, perceptions of the learning environment, and their learning approaches. The students answered questions within the context of subjects I.ypically offered in the secondary schools-mathematics, science, English, German, Japan- ese, history, art, and accounting.

Instruments

The instruments used in this study were the Conceptions of Learning Inventory (COLI; Purdie & Hattie, 1997), the Individualized Classroom Environment Questionnaire (ICEQ; Fraser, 1990), and the Learning Process Question- naire (LPQ; Biggs, 1987).

Conceptions of learning. Purdie and Hattie ( 1997) developed the 45-item COLI to measure secondary students’ conceptions of learning. They categorized learning conceptions into the following nine categories: (a) increasing one’s knowledge, (b) memotizing and reproducing, (c) means to an end, (d) understanding, (e) seeing something in a different way, (f) personal fulfillment, (g) duty, (h) process not bound by time or context, and (i) developing social compe- tence. For the purposes of this study, we selected items that represented qualitative, quantitative, and experiential perspectives on learning. The items for a qualitative perspective originated froni learning as understanding, learning as

perceiving something in a different way, learning as personal fulfillment, and learning as developing social compe- tence. Quantitative perspective items came from learning as an increase in knowledge and learning as remembering and reproducing. We drew experiential perspective items from learning as a process not bound by time or content and learning as a means to an end.

We rated the items for each perspective by subject on a 6- point Likert-type scale (6 = very strongly agree to 1 = strongly disagree). To check the adequacy of the factor structure for the three perspectives for this sample, we used the procedure described by Burnett and Dart (1997). They suggested that the number of factors, which are believed to constitute a scale, should be stipulated when validating the structure of an existing instrument. They also recommend- ed the use of a “maximum likelihood” oblimin rotation method specifying the number of factors to be extracted. Burnett and Dart compared that methodological approach to confirmatory factor analytic procedures and found little difference in the solutions produced. That resulted in the retention of seven items for the qualitative perspective, seven items for the quantitative perspective, and six items for the experiential perspective. Reliability scores for the three measures were: qualitative, a = .88; quantitative, a = .80; and experiential, a = 0.74.

Classroom learning environment. We used the short form of the ICEQ, which consists of 25 items, 5 on each dimension-personalization, participation, independence, investigation, and differentiation-to gather students’ perceptions of their learning environments. Items for each dimension were rated by respondents on a 5-point Liken-type scale (5 = very open to 1 = almost never). We evaluated the adequacy of the factor structure of the ICEQ for this sample with the Burnett and Dart (1 997) procedure. That resulted in the fol-

Figure 2. Proposed Structural Model I PRESAGE PRESAGE

(conceptions) (contextual) PROCESS

Nore. QUAL = qualitative conceptions of learning: QUAN = quantitative conceptions of learning: EXP = experiential conceptions of learning; PERS = personalization: INV = investigation: DEEP = deep approach: SURF = surface approach. I

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lowing make-up: 5 items for personalization and 4 items for investigation. For the purposes of this study, we analyzed only the data from the personalization and investigation measures. We selected personalization as the best measure of the affective climate of the learning environment because it contained opportunities for individual students to interact with the teacher as well as to show concern for their personal welfare and social growth. We used investigation for the most appropriate measure of the cognitive dimension of the learning environment because it emphasized skills and processes of inquiry and their use in problem solving and research. The internal consistencies of the two items were acceptable: personalization (a = .76) and investigation (a = .72).

Approach to learning. The LPQ contains six components of six items each: three of the components measure students' motives for studying (surface, deep, and achieving), and three of them measure corresponding learning strategies adopted by students (surface, deep, and achieving). The corresponding components for motive and strategy can be combined to produce a score representing approaches to learning-surface, deep, and achieving. Items are rated by respondents on a 5-point Likert-type scale (5 = always or almost always true of me and 1 = never or rarely true of me). Only surface approach and deep approach scores were used in this study. We also checked the factor adequacy of the LPQ for this sample with the Burnett and Dart (1997) procedure; seven items were retained for deep approach and six items were kept for surface approach. Reliability scores were a = .76 for deep approach and a = .67 for surface approach.

Analysis

We analyzed the proposed structural model with the maximum likelihood estimate of parameters in LISREL 7 (Joreskog & Sorbom, 1989). Covariance matrices were used to evaluate the goodness-of-fit of the hypothesized structural model. Bagozzi and Heatherton (1994) suggested a method in which subsets of items within factors are summed to create aggregate variables, and they proposed that it is appropriate to have two aggregate variables per factor when the number of measured items per factor is similar to those in the present study (four-seven items per factor).

Goodness-of-fit. There is a lack of consensus among the- orists concerning how best to evaluate the extent to which a proposed model accounts for a set of observed variances and covariances. Most investigators of existing indexes (Marsh, Balla, & McDonald, 1988; Tanaka, 1993) suggested a range of indexes that collectively indicate the efficacy of the proposed model. Hoyle and Panter (1995) suggested using chi-square and the goodness-of-fit index (GFI) as indexes of absolute fit, Tucker and Lewis's (1973) index (TLI) as an incremental fit index, and McDonald and Marsh's (1990) relative noncentrality index (RNI). The four indexes are reported in the Results section of this study.

No interaction or reciprocal effects were tested directly. If such effects existed, they would be identified by the modification indexes produced by the statistical program. LISREL 7 provides a value of the modification index (M I) for each parameter in the model that is fixed at 0, that is, each potential path that is not present in the model tested. The MI represents the improvement in the overall chi- square test of model fit that would occur if that specific parameter were freed. A large M I indicates that if the corresponding parameter were introduced to the model, a significant improvement in fit would occur.

Results and Discussion

Proposed Structural Model

Table 1 reports the descriptive statistics for the latent variables. Figure 3 indicates the weights obtained from the completely standardized solution for the proposed structural model. We used completely standardized coefficients for comparison of relative effects of each of the paths. All parameters were significant at the .05 level, as were the hypothesized paths (except those from personalization to deep approaches), and those that had the predicted signs (except quantitative to deep approaches, which were positive instead of negative as predicted). The indexes of overall fit suggest that the model fit the data reasonably well, ~ ~ ( 6 2 , n = 457) = 150.62, p < .001, GFI = .953, TLI = .942, RNI = .960. The size of the modification indexes indicates that there were no interaction effects.

Model Respecijcation

We omitted the nonsignificant path from personalization to deep approach and retested the model. All paths were significant at the .05 level. That model fit the data in a way similar to the model originally proposed, ~ ~ ( 6 3 , n = 457) = 153.07, p c .001, GFI = .953, TLI = .942, RNI = .959 (see Figure 4). The correlation matrix of the latent variables is reported in Table 2.

Table 1.-Descriptive Statistics for Latent Variables

Variable M SD

QUALa QUANa E X P PERSh INVb DEEPh sum

4.20 0.99 4.30 0.79 4.93 0.7 1 3.59 0.80 2.93 0.76 3.50 0.63 2.94 0.73

"Range = 1 4 , "Range = 1-5. Nofe. QUAL = qualitative conceptions of learning; QUAN = quantitative conceptions of learning; EXP = experiential conceptions of learning; PERS = personalization; INV = investigation; DEEP = deep approach; SURF = surface approach.

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M a d A p r i l 2OOO [Vol. 93(No. 4)]

Figure 3. Completely Standardizod Coefilcients for Parameters in the Proposed Model

PRESAGE PRESAGE (conceptions) (contextual)

PROCESS

Nofr. QUAL = qualitative conceptions of learning; QUAN = quantitative conceptions of learning; EXP = experiential conceptions of learning; PERS = personalization; INV = investigation; DEEP = deep approach; SURF = surface approach. NS = not significant at .05.

Figure 4. Revised Model With Completely Standardized Coemcients at .05

PRESAGE (conceptions)

PRESAGE (contextual)

PROCESS

Note. QUAL = qualitative conceptions of learning; QUAN = quantitative conceptions of learning; EXP = experiential conccpim of leaning; PERS = personalization; INV = investigation; DEEP = deep approach SURF = surface approach.

Results of this study indicate the important associations that exist between conceptions and approaches to learning. Students who report qualitative conceptions are likely to use deep approaches to learning, and those conceptions are likely to suppress the use of surface approaches, likewise for students espousing experiential conceptions of learning. On the other hand, students who have quantitative conceptions of learning will most probably use surface approaches. However, there is also a positive relationship between quantitative conceptions and deep approaches to learning.

In addition, students who report qualitative conceptions

are likely to perceive the classroom learning environment as high in personalization, and to a lesser extent, investigation. Classrooms perceived as high in personalization are associated with the use of investigative skills and strategies, which, in turn, influence the use of deep approaches to learning. Thus, the relationship between personalization and investigation in classroom environments mediates the relationship between qualitative conceptions of learning and deep approaches to learning. Qualitative conceptions, therefore, have both a direct and indirect effect on deep approaches to learning. The use of

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Table 2.-Correlation Matrix of Latent Variables

Variable PERS INV DEEP SURF QUAL QUAN EXP

PERS 1 .Ooo INV .488 1.OOO DEEP ,283 .382 1.Ooo SURF -.076 -.069 -.407 1.OOO QUAL ,315 .288 .616 -.240 1.ooO QUAN .203 .185 .507 -.004 .642 1.ooO EXP” .217 .198 .590 -.253 ,687 ,650 1.ooO

Note. See definitions for acronyms at bottom of Table 1

surface approaches to learning is likely to preclude the adoption of deep approaches.

In this study, we also found that if teachers require their students to develop meaning and understanding of their subjects through deep approaches to learning, then students must hold qualitative or experiential conceptions of learning. Although there has been little research on secondary students’ conceptions of learning (Unger, 1993), research at the university level indicates that the majority of students hold quantitative views (Taylor, 1994). Ramsden (1988) suggested in his relational view of education that improving learning involves changing students’ conceptions of learning. However, that task is not an easy one. It is not simply a matter of telling students that learning is about meaning and understanding; the teaching context and the classroom environment must be congruent with that view. That is, the classroom environment, the teaching strategies, and the assessment procedures must reflect the qualitative view. Therefore, classroom environments and teaching approaches according to constructivist learning theory are required; Biggs ( 1996) referred to this as “constructive alignment.” Dart ( 1998) and Dart and Clarke ( I99 1) provided examples of successful attempts of that type of teaching at the university level.

The contrary finding that quantitative conceptions of learning are positively related to deep approaches to learning needs explaining. Biggs (1993) described an intention to accurately recall information that has already been under- stood, for a high stress situation such as a debate or exami- nation, as possibly involving rote learning. In that content, however, he stated that it could be part of a deep approach. He referred to the work of Tang (1991), who identified such an approach as “deep memorizing.” Biggs also cited the results of studies by Hess and Azuma (1991) and Marton, Tse, and Dall’Alba (1992), who found that Japanese and Chinese students believed that understanding may result from memorization “ . . . and as the intention here is clear- ly to deep understanding, a memorization strategy in this case becomes part of a deep approach” (p. 7). Perhaps for some students who memorize material after having learned it, memorization is associated with deep approaches to learning, particularly for Southeast Asian students.

In this study, some items for a quantitative perspective were selected from the learning as remembering and reproducing category of Purdie and Hattie’s (1 997) questionnaire. Furthermore, one of the schools from which students were selected for the study had a large population (3 1 %) of South- east Asian students. The combination of items representing learning as memorizing and a high proportion of Southeast Asian students in the sample may account for the positive relationship between quantitative conceptions of learning (as defined in this study) and deep approaches to learning.

However, students’ conceptions of learning, and consequently their approaches to learning, may have been heavi- ly influenced by the teaching they experienced (Patrick, 1992; Ramsden et al., 1989; Trigwell, Prosser, & Lyons, 1997). If teachers operate from quantitative perspectives on teaching and learning, then it is highly likely that their students will hold quantitative views on learning. Perkins and Blythe (1993) stated that all teachers say that they teach for understanding, but few of them do in any sustainable way. If that finding is true, then teachers’ conceptions of learning and teaching must be altered before any change can result in their students’ conceptions. Gow and Kember (1993) reviewed the research in that area and asserted that altering teachers’ conceptions is a challenging task. They suggested a three-stage program to facilitate such change: (a) diagno- sis of conceptual frameworks, (b) provision of a period of disequilibrium and conceptual conflict, and (c) reconstruc- tion and reformation as necessary. Bowden (1988) described a successful workshop that he developed to change tertiary teachers’ conceptions.

Our results also indicate that simply providing a learning environment in which students’ feelings are considered, individual interactions with students occur, and students are helped when needed, by itself has no direct influence with the adoption of deep approaches to learning. Such an affective climate is likely to lead to the promotion and use of investigative strategies that facilitate problem solving, and it is that association that increases the probability of the adoption of deep approaches to learning. Such dimensions of the learning environment are more likely to be perceived by students who have qualitative conceptions of learning.

The findings also support Meyer and Muller’s (1990)

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assertion that deep approaches to learning are much more strongly related to perceptions of the learning environment than are surface approaches. Their finding is indicated by the significant path from personalization to investigation and from investigation to deep approaches. There are no significant paths from personalization or investigation to surface approaches.

Conclusion

In this study, we suggest two ways of helping teachers to facilitate their students’ search for meaningful learning. First, teachers need to help their students develop qualitative conceptions of learning, that is, that learning is about developing meaning and understanding. That view must not only be stated, but also it must be evident in the daily classroom teaching and learning processes. Teaching strategies and assessment methods must be congruent with that perspective, which might require that teachers change their opinions on teaching and learning. Second, teachers can promote deep approaches to learning through the creation of learning environments that students perceive as safe, sup- portive, and that offer helpful relationships. Teachers also can present opportunities for exploration, inquiry, and experimentation by providing problems to be solved.

Experiential conceptions have a positive influence on deep approaches and a negative influence on surface approaches. Consequently, teachers should be aware that daily experiences and activities are also important to facilitate deep approaches to learning. Teachers can capitalize on that relationship by making their teaching relevant with examples with which students can easily identify. However, teachers need to remlember that students require both the skills and the will to learn (Pintrich & de Groot, 1990). Stu- dents may want to learn, but they may lack the know-how to do it effectively; that is, they may not possess the appropriate learning skills. ‘Thus, any attempts to structure classroom learning environments in ways that will facilitate deep approaches to learning must ensure that students have the requisite learning strategies to take full advantage of such teaching and learning experiences.

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students' conceptions of learning, the classroom environment, and approaches to learning

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