<\/span><\/h3>\nThere are several theoretical models of Attribution Theory. Heider (1958) is seen as the originator of the Attribution Theory. He noted that there are three determinants of achievement: Ability, effort and task difficulty. He regards ability and effort as internal factors, whereas task difficulty is seen as an external causal factor affecting the outcome. Heider (1958) and Rotter (1966) were concerned with the perceived causes of success and failure and their locus of causation. Heider\u2019s studies were beneficial to understand how one may predict and modify future behaviour.<\/p>\n
Kelley (1967) identifies cognitive factors that are considered when people make internal or external attributions. According to Kelley (1967), people make attributions by using the co-variation principle. Three kinds of co-variation information are Consensus, distinctiveness and consistency.
\nConsistency refers to whether or not an actor\u2019s behaviour in a situation is the same over time: distinctiveness refers to whether or not a person\u2019s behaviour is unique to the specific situation: consensus refers to whether or not other people in the same situation tend to respond like the actor. Highly consistent behaviour is likely to result in external attributions when distinctiveness and consensus are high and internal attributions are most likely when distinctiveness and consensus are low. For example, student A\u2019s outcome in an examination is persistent over time (high consistency), not unique to the other class members (low distinctiveness), and unlike the outcome of his classmates (low consensus). The teacher would be more likely to make an internal attribution and conclude that student A\u2019s failure is due to his personal disposition (Soriano-Ferrer & Alonso-Blanco, 2019).<\/p>\n
Bernard Weiner\u2019s Attributional Model: Weiner (2010) created the framework that is used today in respect of academic achievement. Weiner stressed the relationship between a person\u2019s causal attributions for success and failure and academic achievement. The most prevalent factors that people use to justify their successes or failures are ability, effort, task difficulty and luck (Mudhovozi, Gumani, Maunganidze & Sodi, 2010; Fishman & Husman, 2017). The causes, ability and effort are considered as internal factors since they originate within the person, while task difficulty and luck are seen as external causatives, as they originate outside the person (Weiner, 2010; Igbaakaa, 2019).<\/p>\n
These perceived causes are classified into three causal dimensions: stability shows how stable the perceived cause is, locus investigates whether the cause is internal or external and controllability examines whether or not the perceived cause can be controlled (Basturk & Yavuz, 2010:1940).<\/p>\n
Ability is stable and an internal factor over which the learner does not have much control. Task difficulty is stable, external and beyond the learner\u2019s control. The effort is unstable, internal and the learner has control over this factor. Luck is unstable, external and the learner has almost no control over the chances of manifestation (Batool, Muhammad, & Qaisara, 2012; Basturk & Yavuz, 2010).<\/p>\n
The locus and stability of causal attribution determine the expectancy of future success. When a student attributes his\/her failure to a stable factor such as subject difficulty (external, stable and uncontrollable), he\/she is likely to expect future failure. If failure is due to a lack of ability (internal, stable cause) or an unfair teacher, then future failure can be anticipated. However, if failure is perceived as due to an unstable factor (bad luck or insufficient effort exerted on the task), failure will not be anticipated. Locus and controllability, on the other hand, relate to affective states or the emotional value of outcomes. Locus influences feelings of pride and self-esteem, and controllability jointly influences feelings of guilt or shame with the locus of causality. Thus, locus and control determine whether guilt or shame is experienced following failure; and these are independent dimensions.<\/p>\n
These attributional causes play a major role in moulding future expectancies and students\u2019 motivation to learn (Basturk & Yavuz, 2010). Students\u2019 beliefs about their causes for success and failure greatly influence their academic achievement. There is a relationship between attributional patterns and achievement in school. Nenty (2010) noted that an internal attributional style leads to better achievement, whereas, external attributions are related to negative achievement.<\/p>\n
Internal attributions ascribe the causes of behaviour to personal dispositions, traits abilities and feelings. External attributions ascribe the cause of behaviour to situational demands and environmental constraints. Internal and external attributions have a tremendous effect on one\u2019s everyday interpersonal interactions (Weiner, 2010). These attribution patterns influence the learner\u2019s self-concept, expectancies for future situations and motivation. Learners are strongly motivated by the pleasant outcome of feeling good about themselves. The amount of effort the student exerts on a given task is determined by the person\u2019s attributions for their success or failure.<\/p>\n
Weiner\u2019s Attribution Theory explains why students react in certain ways based on their achievement and expectations. He notes that people ascribe their own success or failure to future expectations of success, emotional reactions and selfesteem (Batool et al., 2012). Learners could benefit from the ability to reason about the causal relationship for their successes or failures and such ability helps them to predict the outcomes of their actions (Cook & Artino, 2016).<\/p>\n
Attribution researchers have found that people often make misjudgements when evaluating the behaviour of others and tend to attribute the other\u2019s behaviour to their inner dispositions which are unchangeable and uncontrollable. Hence, these people believe that they have control over their lives (Mudhovozi et al., 2010). This tendency is known as the fundamental attribution error where people are inclined to over-estimate the role of internal causes (person\u2019s traits or attitudes) and underestimate the role of external factors in explaining other people\u2019s behaviour. It is the tendency to attribute a student\u2019s failures to a lack of personal characteristics e.g. low aptitude (internal) rather than to situational causes. Another student\u2019s success is explained in terms of an easy test (external) rather than internal causes. A student\u2019s own failure is rationalized in terms of external causes such as task difficulty or bad luck (Mudhovozi et al., 2010). Success is attributed to internal factors: ability (internal, stable, and uncontrollable) and effort (internal, unstable, and controllable).<\/p>\n
Consistent with many previous studies on attributions and achievement, students show self-serving attributional patterns of attributing their highest marks to internal causes (effort, ability) more than their lowest marks, and attributing their lowest marks to task difficulty more than their high marks. However, counterto self-serving bias, they invoke luck for their best marks rather than their worst marks (McClure et al., 2010). By claiming credit for success, the claim gives the student a sense of control over what happens in their lives and in other people\u2019s lives. In-group members\u2019 attribution of failure to the situation is a common tendency engaged to protect the student\u2019s self-esteem, and not feeling demoralised when they fail. This bias is universal across all cultures, genders and age groups (McClure et al., 2010; Mudhovozi et al., 2010).<\/p>\n
Manzoor & Mahmood (2019) found that high achievers attributed their success and failure outcomes more to effort and ability. Low achievers, on the other hand, attributed their low marks and weak performance to luck and task difficulty. The results in Manzoor and Mahmood\u2019s study were consistent with Weiner\u2019s theory that high achievers associated their outcomes with internal factors. Low achievers associated their outcome with external factors (luck and task difficulty).<\/p>\n
McClure et al., (2010) contend that grade 11 students who attributed their success to internal factors (effort and ability) attained higher marks. In contrast, students who attributed their success to luck, family, and friends gained lower scores. This finding is consistent with Weiner\u2019s (2010) theory that external attributions for success lead to poorer achievement.<\/p>\n
The results of McClure\u2019s study also showed that failure attributions to lack of effort and to the teacher lead to the attainment of higher scores. On the other hand, attributing failures to family and friends lead to lower marks. Attributing failures to the teacher is consistent with the self-serving bias where people attribute success to internal causes and failure to external causes as such attribution enhances the self-esteemof the student.<\/p>\n
Attributional theories contend that effort attributions (controllable) increase perseverance and that it is the strongest predictor of achievement (Weiner, 2000; Batool et al., 2012). They further claim that ability attributions for success and failure predicted achievement better than effort attributions. Attributing success to ability leads to pride, self-efficacy and persistence. Other studies also suggest that ability attributions for success have been related to higher academic achievement (Frijters et al., 2018; Cuevas et al., 2014).<\/p>\n
If success is attributed to an internal and controllable cause the student feels proud and increased self-esteem. The increased expectation of high future outcomes is associated with positive emotional reactions and determines the subsequent outcomes, that is, behaviour depends on thoughts as well as feelings. Failure and lack of ability (internal, uncontrollable) evoke feelings of shame, embarrassment and humiliation (Weiner, 2000). When learners\u2019 failure is attributed to internal factors, self-esteem can be diminished. In classroom settings, students need a sense of positive self-esteem and competence and they need to be reassured; otherwise, they give up and say that the task is too difficult (Rusillo & Arias, 2004:101; Nathaniel, 2019). If teachers set very difficult tasks, expanded effort on the part of the students will not be relevant.
\nWhen students realize that success is controlled by task difficulty the students aim to control such success.<\/p>\n
When teachers praise students for success with little effort, such acknowledgement teaches learners not to work hard. If a teacher criticises learners for failure that could have been achieved with effort, such admonition communicates to students that they have the ability to succeed and they should put in the requisite effort (Batool et al., 2012).<\/p>\n
Other studies (Chen et al., 2016; Fwu et al., 2018) have shown that when students attribute their failure in the test to lack of effort rather than blaming it on external influences, they are more likely to perform better in future tests. When students make stable attributions and believe that they succeeded because of the effort that they put in, then they will put in more effort into the task at hand in order to succeed, and if they believe it was due to ability, they will then expend more effort on the task in order to succeed. Students will not be interested if they believe that their success or failure is due to external factors. Teachers must encourage students to believe that they have the ability (stable) and that they can control effort. Thus, ability combined with effort will lead to success.<\/p>\n
Controllability: he\/she feels failure is uncontrollable, becomes aggressive, and feels helpless and reluctant to work as he assumes he will not produce any quality work. A teacher may convey unintended messages when he\/she shows annoyance with unsuccessful students. Such annoyance gives the message that the student had the ability to perform but did not succeed. When the teacher shows sympathy, such an attitude tells the student that even with the effort he\/she does not have the ability to succeed. High school or college students want to preserve their egos and when they fail, they claim that they did not apply sufficient effort when completing a task. When they succeed, on the other hand, they want to be considered \u201csmart\u201d (Fwu et al., 2018).<\/p>\n
Attributions are complicated and have important implications for how people see themselves and others. However, certain attributional biases lead to inaccurate judgments of whether the cause is internal or external.<\/p>\n
<\/span>3. Methods<\/span><\/h3>\nA mixed-methods research design was utilized with qualitative and quantitative characteristics as proposed by Creswell and Clark (2011). The qualitative sample was much smaller than the quantitative sample and it helped the researcher to get a better in-depth discussion through interviews and a \u201crigorous\u201d quantitative investigation of the topic.<\/p>\n
The explanatory design as highlighted by Creswell and Clark (2011) for the mixed-method study was used. The quantitative data was first collected through questionnaires, thereafter it was analysed, and then this was followed up with smaller qualitative data collection.<\/p>\n
Research strategy: Instruments used in this research study were questionnaires and interviews. The quantitative method used in the research is the survey design. The data collected in this study were through questionnaires (quantitative) and face-to-face interviews (qualitative) as supported by Maree (2010). The study investigated the learners\u2019 ability, effort, task difficulty and luck attribution styles and their effect on achievement in science.<\/p>\n
Population and sampling<\/strong>: Table 1 below provides details about the population and sample. The target population included high schools in two educational regions of the North-West Province of South Africa (n=60).<\/p>\nThe sample used in the study was s=30.<\/p>\n
A systematic random sampling method was used to pick a sample of 1773 Grades 10 to 12 science learners from 30 of the 60 schools. This sample was representative in that it constituted 15% of the population. In total 1773 questionnaires were distributed to the learners.<\/p>\n
Purposive sampling technique was used to select 5 teachers from the teaching staff and 5 Physical Scienceslearners for interviews. Edmonds and Kennedy (2013) advise that purposive sampling provides in-depthdata pertaining to the research questions posited in a study. Hence, there were 10 interviews that wereconducted to supplement the learners\u2019 views on the attribution stance.<\/p>\n
Table 1. Population and sample<\/b><\/span><\/span><\/p>\n\n- \n
\n\n\n| Schools in NW<\/span><\/span><\/td>\n | Schools<\/span><\/span><\/td>\n | Learners <\/span><\/span><\/td>\n<\/tr>\n\n| Population<\/span><\/span><\/td>\n | 60<\/span><\/span><\/td>\n | 11 748<\/span><\/span><\/td>\n<\/tr>\n\n| Sample<\/span><\/span><\/td>\n | 30<\/span><\/span><\/td>\n | 1 773<\/span><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/dd>\n<\/dl>\n Instrumentation<\/strong>: Quantitative data were collected using close-ended questions, while open-ended questions were utilized in the qualitative interviews as supported by Edmonds and Kennedy (2013). The Attribution Questionnaire was used to identify the attributional styles of learners on the science achievement oflearners. The Likert Scale was used to measure the strength of four attributional factors: ability, eff ort, taskdiffi culty and luck. The percentage achieved in the physical science test was indicated in the questionnaire.Learners who achieved 70% and above in their science test were classified as high achievers, whereas learners who achieved below 39% were classified as low achievers.<\/p>\nThe physical science learner respondents in this study chose one of four options in the Attribution Ques-tionnaire to indicate the type of attributions that aff ect their achievement in science. There were four fac-tors of attributions which included: ability, effort, task difficulty and luck attribution. For example, the respondents indicated whether their science test mark was due to \u2018no ability\u2019, \u2018little ability\u2019, \u2018more ability\u2019 or \u2018a lot of ability\u2019. For the qualitative interviews, 5 teacher respondents and 5 learners gave their opinions on whether learners had the ability, exerted a lot of effort in the preparation of the physical science test andwere interested in science. They also gave their opinions on the test difficulty and whether or not they werelucky in achieving the results that they did.<\/p>\n Data collection and administration<\/strong>: The questionnaires were hand-delivered after prior appointments had been made with the principals at the respective schools. They were administered to 1773 respondents from the two educational regions of the North-West Province. The purpose of the study and the procedures in completing the questionnaires was explained with the assistance of teachers at the relevant schools. The purpose of the interview, data-collection technique of recording interviews and duration of the interviews was explained to the teachers and learner respondents at each school. All respondents were proficient in English and they were able to express their feelings and opinions freely.<\/p>\nRespondents explained how important each of the attributional factors was in affecting their achievement in science. During the interviews, the researcher probed respondents in a bid to get a full understanding ofthe learners\u2019 explanations on how attributions influenced their achievement in science. The teachers an-swered questions related to the attributional styles of Grades 10 to 12 physical science learners. The interviews were captured for transcription, analysis and interpretation. Questionnaires were collected immediately after administration.<\/p>\n Data processing<\/strong>: Quantitative and qualitative techniques were applied in analysing data. Statistical consultants assisted in organizing and analysing data.<\/p>\nQuantitative data were gathered from the questionnaires which were administered to the science learners. Interview questionnaires were transcribed qualitatively. <\/span><\/p>\nData were interpreted using simple descriptive statistics and raw data were <\/span>captured and presented in tables and bar charts.<\/span> Frequencies and percentages were calculated to represent data.<\/span> Statistical <\/span>methods such as the Chi-Square Test, Analysis of Variance (ANOVA) and <\/span>the Pearson coefficient of correlation test were used to interpret the data. <\/span><\/p>\nEthical considerations<\/strong>: Letters of consent to conduct the research study were obtained from: the District <\/span>Man<\/span>ager to go to <\/span>sampled <\/span>school<\/span>s<\/span>; school principals to conduct the research; from school principals to conduct the interviews with learners and teachers. Respondents gave permission for recording of interviews, were made aware of the purpose of the interviews that their participation was voluntary, and they were assured of anonymity and confidentiality.<\/p>\nIn this study, the researcher ascertained that all respondents were proficient in English so that they could complete the questionnaires and the learners were able to voice their feelings and opinions freely.<\/p>\n Trustworthiness<\/strong>: To ensure the reliability and validity of the instruments in this study, a pilot study was conducted by using a different sample of learners from Grades 10 to 12 who were not included in the final testing. The questionnaires were pre-tested at two high schools to establish the clarity and relevance of the questions. To supplement the questionnaire findings, interviews were conducted to validate the data quality.<\/p>\n<\/span>4. Results<\/span><\/h3>\nFigure 1 represents the four attribution factors in terms of levels of attributions. The four levels are categorized on a continuum from \u2018no\u2019, \u2018little\u2019, \u2018more\u2019 to \u2018high\u2019 levels of each factor of attribution. The factors of attribution are as follows: ability, and effort (internal attributional style), task difficulty and luck, (external style of attribution).<\/p>\n The respondents were requested to rate each one of the four attribution factors regarding their influence on their performance and thereafter rank the attribution in its relation to science achievement.<\/p>\n Results in Figure 1 indicate that most of the science learners made the highest attribution to \u2018more ability\u2019 (43%), and \u2018more effort\u2019 (46%). The attribution factors ability and effort constitute an internal attribution style therefore, most science learners made high internal attributions. In addition, the figure indicates that the science learners used less external attributions by making less task difficulty (54%) and less luck (42%) attributions.<\/p>\n ![\"\"](\"http:\/\/ensovoort.co.za\/wp-content\/uploads\/2021\/11\/causal_fig1.jpg\") <\/p>\n
The internal (ability and effort) and external attributions (task difficulty and luck) were assessed in terms oflow achievers (0-39%) and high achievers (70-100%) in science. Table 1 demonstrates the ability attributions of high, average and low achievers. In terms of high versus low achievers, the current study demonstrates that high achievers made higher ability attributions (30%) than low achievers (13%). On the other hand,more low achievers (45%) believed that they had no ability in science compared to 10% of high achievers.<\/p>\n Table 1: Ability Attributions of Science learners<\/b><\/span><\/span><\/span><\/span><\/p>\n\n- \n
\n\n\n| Ability Attributions <\/b><\/span><\/span><\/span><\/span><\/td>\n | High achievers %<\/b><\/span><\/span><\/span><\/span><\/td>\n | Average achievers %<\/b><\/span><\/span><\/span><\/span><\/td>\n | Low achievers %<\/b><\/span><\/span><\/span><\/span><\/td>\n | Total <\/b><\/span><\/span><\/span><\/span><\/p>\n %<\/b><\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n\n| No ability<\/span><\/span><\/span><\/span><\/td>\n | 10<\/span><\/span><\/span><\/span><\/td>\n | 45<\/span><\/span><\/span><\/span><\/td>\n | 45<\/span><\/span><\/span><\/span><\/td>\n | 100<\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n\n| Little ability<\/span><\/span><\/span><\/span><\/td>\n | 6<\/span><\/span><\/span><\/span><\/td>\n | 67<\/span><\/span><\/span><\/span><\/td>\n | 27<\/span><\/span><\/span><\/span><\/td>\n | 100<\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n\n| More ability<\/span><\/span><\/span><\/span><\/td>\n | 20<\/span><\/span><\/span><\/span><\/td>\n | 66<\/span><\/span><\/span><\/span><\/td>\n | 14<\/span><\/span><\/span><\/span><\/td>\n | 100<\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n\n| High ability <\/span><\/span><\/span><\/span><\/td>\n | 30<\/span><\/span><\/span><\/span><\/td>\n | 57<\/span><\/span><\/span><\/span><\/td>\n | 13<\/span><\/span><\/span><\/span><\/td>\n | 100<\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/dd>\n<\/dl>\n Table 2 depicts the effort attributions of high, average and low achievers. In terms of high versus low achievers, the current study demonstrates that successful high achievers tend to make higher effort (35%) attributions than low achievers in the failure condition (12%). Furthermore, more low achievers (46%) than high achievers (8%) made a lack of effort attributions.<\/p>\n Table 2: Effort Attributions of Science Learners<\/b><\/span><\/span><\/span><\/span><\/p>\n\n- \n
\n\n\n| Effort attributions <\/b><\/span><\/span><\/span><\/span><\/td>\n | High achievers %<\/b><\/span><\/span><\/span><\/span><\/td>\n | Average achievers %<\/b><\/span><\/span><\/span><\/span><\/td>\n | Low achievers %<\/b><\/span><\/span><\/span><\/span><\/td>\n | Total <\/b><\/span><\/span><\/span><\/span><\/p>\n %<\/b><\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n\n| No effort<\/span><\/span><\/span><\/span><\/td>\n | 8<\/span><\/span><\/span><\/span><\/td>\n | 46<\/span><\/span><\/span><\/span><\/td>\n | 46<\/span><\/span><\/span><\/span><\/td>\n | 100<\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n\n| Little effort<\/span><\/span><\/span><\/span><\/td>\n | 8<\/span><\/span><\/span><\/span><\/td>\n | 65<\/span><\/span><\/span><\/span><\/td>\n | 27<\/span><\/span><\/span><\/span><\/td>\n | 100<\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n\n| More effort<\/span><\/span><\/span><\/span><\/td>\n | 17<\/span><\/span><\/span><\/span><\/td>\n | 68<\/span><\/span><\/span><\/span><\/td>\n | 15<\/span><\/span><\/span><\/span><\/td>\n | 100<\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n\n| High effort <\/span><\/span><\/span><\/span><\/td>\n | 35<\/span><\/span><\/span><\/span><\/td>\n | 52<\/span><\/span><\/span><\/span><\/td>\n | 12<\/span><\/span><\/span><\/span><\/td>\n | 100<\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/dd>\n<\/dl>\n Table 3 displays the task difficulty attributions of high, average and low achievers. In terms of high versus low achievers, more high achievers (35%) than low achievers (15%) found that the test was not difficult. On the other hand, more low achievers (37%) who failed than high achievers (11%) found the science test extremely difficult.<\/p>\n Table 3: Task Difficulty Attributions of Science Learners<\/b><\/span><\/span><\/span><\/span><\/p>\n\n- \n
\n\n\n| Task difficulty<\/b><\/span><\/span><\/span><\/span><\/p>\n Attributions<\/b><\/span><\/span><\/span><\/span> <\/b><\/td>\n | High achievers %<\/b><\/span><\/span><\/span><\/span><\/td>\n | Average achievers %<\/b><\/span><\/span><\/span><\/span><\/td>\n | Low achievers %<\/b><\/span><\/span><\/span><\/span><\/td>\n | Total <\/b><\/span><\/span><\/span><\/span><\/p>\n %<\/b><\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n\n| No task difficulty<\/span><\/span><\/span><\/span><\/td>\n | 35<\/span><\/span><\/span><\/span><\/td>\n | 50<\/span><\/span><\/span><\/span><\/td>\n | 15<\/span><\/span><\/span><\/span><\/td>\n | 100<\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n\n| Little task difficulty<\/span><\/span><\/span><\/span><\/td>\n | 18<\/span><\/span><\/span><\/span><\/td>\n | 65<\/span><\/span><\/span><\/span><\/td>\n | 17<\/span><\/span><\/span><\/span><\/td>\n | 100<\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n\n| More task difficulty<\/span><\/span><\/span><\/span><\/td>\n | 10<\/span><\/span><\/span><\/span><\/td>\n | 71<\/span><\/span><\/span><\/span><\/td>\n | 19<\/span><\/span><\/span><\/span><\/td>\n | 100<\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n\n| High task difficulty<\/span><\/span><\/span><\/span><\/td>\n | 11<\/span><\/span><\/span><\/span><\/td>\n | 52<\/span><\/span><\/span><\/span><\/td>\n | 37<\/span><\/span><\/span><\/span><\/td>\n | 100<\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/dd>\n<\/dl>\n Table 4 reveals the luck attributions of high, average and low achievers. In terms of high versus low achievers, more low achievers (25%), who failed in science, than high achievers (16%) made no luck attributions. On the other hand, more high achievers (29%) and less low achievers (15%) tended to attribute their out-come to high luck. The study indicated that high achievers attributed their success to high luck and low achievers attributed their failure to lack of luck.<\/p>\n Table 4: Luck Attributions of Science learners<\/b><\/span><\/span><\/span><\/span><\/p>\n\n- \n
\n\n\n| Luck Attributions<\/b><\/span><\/span><\/span><\/span><\/td>\n | High achievers %<\/b><\/span><\/span><\/span><\/span><\/td>\n | Average achievers<\/b><\/span><\/span><\/span><\/span><\/td>\n | Low <\/b><\/span><\/span><\/span><\/span><\/p>\n achievers %<\/b><\/span><\/span><\/span><\/span><\/td>\n | Total <\/b><\/span><\/span><\/span><\/span><\/p>\n %<\/b><\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n | | | | | | | | | | | | | | | | | | |