在阿德莱德大学,学生在学期末收到挂科通知时,可能会感到困惑和焦虑。面对这种情况,学校提供了几种补救措施,但如何选择最合适的方案往往让人犹豫不决。将详细在阿德莱德大学出分挂科后,学生可以采取的三种补救措施,以及如何做出最明智的选择。

1. 申诉(Appeal)

对于挂科的学生,申诉是最直接的一种补救途径。如果你认为挂科的原因不公正,例如成绩评定存在误差,或你因为不可抗力因素(如健康问题、家庭紧急情况等)未能完成学业要求,你可以提出正式的申诉。申诉需要提供有力的证据和充分的理由,通常需要在成绩公布后的特定时间内提交。

适用情况

  • 你认为成绩评定存在错误(如考试评分错误、作业评分不公)。
  • 你有足够的证据证明由于特殊情况(如生病、家庭变故等)未能按时完成课程要求。

注意事项
申诉的成功率取决于你能否提供充足的证据,并且申请过程时间紧迫,因此务必提前准备好相关材料。

2. 重修课程(Re-enrolment)

如果申诉未能成功,或者你认为不值得通过申诉来解决问题,另一种选择就是重修课程。在阿德莱德大学,学生通常可以选择重修挂科课程,重新参加课程学习,并通过考试来重新获取学分。重修课程对于一些长期滞留的学术问题尤其有效,它不仅可以帮助你重新获得挂科课程的学分,还可以提升你的学术成绩。

适用情况

  • 你未能通过某些课程,但仍希望提高自己的学术水平。
  • 你认为自己可以在重修过程中进行有效的学习和复习。

注意事项

  • 重修课程可能需要支付额外的学费,并且可能影响你的毕业进度。
  • 如果你挂科的课程涉及核心课程,重修可能对你毕业有直接影响。

3. 选择替代课程(Course Substitution)

有些情况下,挂科课程的可能并不是你未来专业学习的核心,或者你觉得重新修读该课程对你来说不再有意义。阿德莱德大学允许学生在某些条件下选择替代课程来代替挂科课程,继续推进学位的完成。替代课程通常要经过学术顾问的审批,你需要选择一门相似或相关的课程,并获得学校的批准。

适用情况

  • 你希望通过另一门课程代替挂科课程,且该课程符合学位要求。
  • 你希望避免再次修读同一课程,但仍希望顺利毕业。

注意事项

  • 需要与学术顾问沟通,确认替代课程是否能够满足毕业要求。
  • 替代课程可能影响你学位的深度或专业方向,因此需谨慎选择。

如何做出最佳选择?

选择补救措施时,最重要的是根据个人的实际情况做出决策。你需要评估自己的挂科原因、后续学习的能力以及毕业计划。如果你认为挂科是因为不公正的评定或特殊情况,申诉可能是最好的选择;如果你相信自己通过重修可以弥补过失,重修课程则是一个有效的方案;如果你希望尽快调整学业规划,并且挂科课程对你未来学习影响不大,选择替代课程也是一种可行的解决办法。

无论选择哪种补救措施,关键是及时了解阿德莱德大学的相关规定,并在规定的时间内采取行动。如果你对自己的学业计划有任何不确定,建议咨询学术顾问,确保选择对你最有利的方案。

斯笔客教育助你申诉成功,重修课程无忧

如果你正在面临阿德莱德大学的挂科问题,斯笔客教育提供专业的学术咨询与申诉服务。我们的专家团队能够为你提供详细的补救方案,帮助你准备申诉材料,解决学术问题。无论是申诉成功、重修课程还是选择替代课程,斯笔客教育都将为你提供全方位的支持,助你顺利度过学术难关,早日实现学位目标。

上一篇

化学工程专业作为一门应用科学,需要学生掌握扎实的理论知识和实际操作技能。在留学生群体中,化学工程专业的论文写作是非常具有挑战性的,不仅需要学生深入理解相关原理,还要具备一定的分析和解决问题的能力。而针对化学工程专业论文的辅导收费是否符合市场行情,也是很多学生关注的重点问题。将通过详细分析化工专业的论文选题、字数要求、写作技巧等,帮助留学生更好地了解如何写好化学工程专业论文,并对目前的辅导收费标准进行合理评估,以确保大家选择到合适的辅导服务。

论文选题

在化学工程专业的论文写作过程中,选题是决定论文质量的关键。一个好的选题不仅要具备创新性和学术价值,还要紧密结合实际应用。例如,学生可以从新型材料的开发、化工过程优化、能源环保技术等方向进行深入研究。选题范围越有现实意义,论文的写作难度也可能越大。因此,选择适合自己能力范围的课题至关重要。不同导师对选题的要求可能有所不同,这也是留学生需要提前了解的,以确保选题符合导师的期望。

论文字数要求

化学工程专业的论文字数要求通常根据论文类型有所不同。一般来说,毕业论文或学位论文的字数要求较高,通常在8000到15000字不等,而课程论文的字数要求则较低,通常在3000到5000字左右。在写作过程中,留学生要注意合理分配字数,确保每一章节的充实且符合要求。字数的控制还需与论文结构紧密结合,避免在字数不足或超出的情况下影响论文整体的逻辑性和严谨性。

如何写好化学工程专业论文

写好一篇化学工程专业的论文,关键在于以下几个方面

清晰的研究框架:学生需要在论文开头明确提出研究问题和目标,并在论文中通过严密的推理和数据支持,逐步解答这些问题。

数据分析与验证:化学工程论文通常涉及大量的实验数据和理论推导。因此,学生需要保证实验数据的可靠性,合理使用统计工具进行分析,并验证其结论的有效性。

理论与实际结合:在写作中,要强调如何将理论应用于实际工程问题的解决过程。通过实例或实验结果展示解决方案的可行性,是论文的亮点之一。

语言表达与格式规范:论文写作应使用学术语言,确保表达简洁、准确。同时,遵循学校或导师要求的论文格式,尤其是在引用文献、图表标注等细节方面严格按照要求进行。

化学工程专业论文辅导收费是否符合市场行情?

化学工程专业论文的辅导收费因多种因素而有所不同,包括辅导老师的专业背景、辅导的复杂性以及辅导的时长等。一般而言,化工专业的辅导费用相较于其他文科类专业要高一些,这是因为化工领域的论文涉及较多的专业知识、实验分析和数据处理。当前市场上,化工专业的论文辅导价格通常在每小时500元至1500元不等。如果涉及到论文全程辅导或较为复杂的实验数据处理,收费还会更高。

要判断辅导费用是否合理,留学生可以从以下几点进行考虑

导师的学术背景:具备化学工程博士学位或有多年行业经验的导师,收费往往较高,但他们的辅导效果也更为显著。

辅导内容的深度:简单的论文写作建议与全面的论文辅导服务收费会有较大差异。具体来说,越是深入的数据分析、建模、实验设计等,费用越高。

市场对比:留学生可以通过多方比价来了解市场平均水平。对于提供全程论文辅导和批改服务的机构,其收费通常会更高,但能够确保论文的整体质量。

斯笔客教育作为专业的留学生辅导机构,提供化学工程专业论文写作辅导,针对不同留学生的需求量身定制辅导计划,确保学生不仅能顺利完成论文,还能掌握相关的专业技能与知识。如果您正在寻找可靠的化工专业论文辅导服务,斯笔客将是您值得信赖的选择。

下一篇  辅导R留学生程序、 写作data程序

” 辅导R留学生程序、 写作data程序Applied Statistics with R Final Exam 2020-05-22 Exam ID 00003Name:Student ID:4. (a)Applied Statistics with R Final Exam: 00003 2Applied Statistics with R Final Exam: 00003 3Applied Statistics with R Final Exam: 00003 41. Load the data provided in flights.Rdata from **piazza** and select all flights that hadbeen scheduled For departure between 2013-03-15 and 2013-03-19.(a) How many flights (i.e. cases) are in the resulting data set?(b) How many variables does the resulting data set comprise?2. Plot a box plot of the departure delays (variable dep_delay) using the departure airport(variable origin) as grouping variable.(a) Do the three NYC airports show a similar distribution in departure delays?(b) Are the median departure delays for each airport close to zero?(c) Which flight (from Where to where) came the earliest (had the smallest departure delay)?(d) Which flight (from Where to where) has the largest departure delay?3. Create a categorical variable delay with three categories: on time: flights with an arrival delay less than 14 minutes delayed: flights with an arrival delay between 14 and 41 minutes heavy Delay: flights with an arrival delay of more than 41 minutesOrder the variable according to on time, delay, heavy delay.(a) Compute the number of flights in category delay.(b) Compute the number of flights in category on time.(c) Compute the number of flights in category heavy delay.(d) Which delay category is the most frequent one?4. Cross-tabulate the variables origin and delay.(a) What is the most-frequent combination of the two variables origin and delay?(b) Which share of on time flights departed from LGA?(c) Which share of flights departing from LGA have been in delay category on time ?5. Assess the relationship between origin and delay using the 辅导R留学生程序、 写作data程序作业、 辅导程序语言作业-statistic. Calculate theexpected frequencies under the assumption that the departure airport has no effect onarrival delays.(a) Is the relationship statistically significant?(b) Report the score of the 2-test statistic (round to two digits)!(c) Which (sampling) Distribution is used for the 2-test to compute the p-value?(d) For which cells are expected frequencies higher than the observed ones?6. The IATA claims that New York City airports are delaying air traffic. In order to investigatthis claim, check whether departure delays are on average signfificantly higher than arrivaldelays. Use an appropriate Statistical test using the 5% significance level.(a) Which statistical test is most appropriate here?(b) Is the relationship statistically significant?(c) Report the score of the test statistic (round to two digits)!(d) Can you confirm the IATA claim?Applied Statistics with R Final Exam: 00003 57. The IATA wants to know whether all three airports in New York City operate at the sameperformance level regarding departure delays. Use a suitable statistical test to check this!(a) Is there a difference in performance regarding departure delays between the threeairports?(b) Which statistical test did you use?(c) Report the test statistics score!(d) Report the total sum of squares accounted by origin!(e) Which airports differ statistically significantly in their performance?(f) Which airport has the best performance?8. In the following, you restrict your analysis to flights that have an arrival delay of at least 34minutes. Create the corresonding data subset.(a) How many flights are in this subset?(b) How many flights have been excluded by this procedure?9. You are interested in the (linear) relationship between departure delay and arrival delay.(a) Would you expect any relationship? If yes, which one? / Calculate the kendall correlationcoefficient and comment on it.(b) Draw a scatter plot of number of arrival delay against departure delay and commenton it. / Discuss whether this correlation coefficient is an appropriate measure in thissituation!10. Compute a linear model for arrival delay as dependent variable using the following variablesin the data set as predictors: dep_delay, origin, air_time, carrier, dest, month,day. Call this model flights.lm!(a) According to the ANOVA table, is the predictor dest statistically significant (at least) atthe 10% significance level?(b) Looking at the regression coefficients, briefly discuss what the coefficients for originJFKand originLGA mean?(c) How good does this model fit? On what do you base your judgment?(d) Check whether the residuals of this model follow a normal distribution! Do they? Whichtool did you use for checking this?11. Starting with the null model and taking the model flights.lm as upper bound, run a stepwisemodel selection procedure to find the best model according to the AIC criterion. Callthe resulting model flights.lm.best!(a) Which predictors are included in the optimal model?(b) Report the adjusted R-squared of the final model?(c) Report the AIC of the final model?(d) Using an F-test check whether the final (= best according to automatic variable selection)model is significantly different from the model using the predictors as computedin the previous exercise?(e) Which predictors are included in the model computed in the previous exercise that arenot included in the final model obtained by the automatic procedure?(f) According to the final model, by how many minutes more is a flight at arrival delayed ifit departs 10 minutes later (all other things being equal).Applied Statistics with R Final Exam: 00003 6(g) According to the final model, which carrier is the best to minimize arrival delays.(h) Amend the final model by adding an interaction term between origin and air_time.Is the interaction term significant at the 10% level? According to this model and allother things equal, by how much will departure delays differ for two flights having adifference in air time of 100 minutes and one leaving from EWR, the other from JFK(all other things being equal)?12. You now want to generate a classification model that tells you whether a flight is delayed atarrival more than 92 minutes using the predictors in the model flights.lm.best. Use theprobit link here! Call the model flights.classbin!(a) Which error distribution have you chosen to create this model?(b) According to the Wald tests (Table of coefficients): which predictors are significant (atleast at the 5% level)?(c) According to the Likelihood-Ratio Test (LR-test as given in the Deviance Table): whichpredictors are significant (at least at the 5% level)?(d) Report the residual deviance of your model?(e) Report the Null deviance of your model?(f) Using a 2-test check whether your model is significantly better than the null model?(g) According to your model, how does the air time of a flight influence the likelihood of itbeing more than two hours delayed?(h) Based on your models fitted probabilites for being more than two hours delayed createan indicator for delayed/not delayed flights using the probability 0.5 as threshold.Create a frequency table of the predicted and the observed delay indicator. Calculateall misclassification rates.13. Using the model flights.classbin predict the probability for being more than two hoursdelayed at departure using the average scores of numeric predictors in the model for carrierUA (United Air Lines) and destination ORD (Chicago Ohare International) and origin JFK.14. Again using the model flights.classbin, you now want to investigate the specific dependencyon the hour of the day. In case hour is not yet included in the model, update themodel by adding this predictor. Generate new data such that you have the hours from 5 to23 in increments of 1. The other numeric predictors enter again with their mean score intothe prediction in the model for carrier UA (United Air Lines) and destination ORD (ChicagoOhare International). Compute the predictions and average them.15. What is the name of the R function for multinomial logistic regression?如有需要,请加QQ:99515681 或邮箱:99515681@qq.com “

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