Revista Argentina de Clínica Psicológica 2020, Vol. XXIX, N°1, 1045-1051
DOI: 10.24205/03276716.2020.147 1045
A
SSOCIATION
M
INING BETWEEN
I
DEOLOGICAL AND
P
OLITICAL
T
EACHING AND
M
ENTAL
H
EALTH
Chunqian Bi
*Abstract
In the new era, it is highly necessary to renovate the teaching models of ideological and political (I&P) education and mental health education, in light of the association between the two subjects. With the aid of data mining, this paper sets up a correlation analysis system I&P teaching and mental health. Then, the Apriori association mining algorithm was introduced to the system for data preprocessing, association mining and data analysis. The results from data analysis show that I&P education and mental health education agree well in both objects and ultimate goals, and the two subjects complement each other. The research findings provide new clues to classroom teaching of the I&P and the promotion of mental health of college students.
Key words: Data Mining, Ideological and Political (I&P) Education, Mental Health, Apriori Association Mining Algorithm.
Received: 16-04-19 | Accepted: 12-09-19
INTRODUCTION
Along with the advancement of education concepts and models in universities, students distinctively cling to political consciousness of
advocating the socialism with Chinese
characteristics. In the formation of world outlook, values and view of life, they are also more subjected to psychological factors such as social pressure, interpersonal relationship and affective communication. To improve the ideological and political and mental health education, it is significant to cultivate synthetical diathesis and skills of students (Brown & Murphy, 2018). The traditional ideological and political teaching in higher education still presents a dogmatic and simplified model. It has fallen short of individualized demands of students and what' s needed in the ideology and morality cultivation in the new era. However, mental health education does a little in shaping
School of Marxism Xinjiang University, Xinjiang Uygur autonomous region, Urumqi 830046, China.
E-Mail: [email protected]
students’ personality and their formation of
values, much less in improving their ideological and political consciousness. The two education models still need to be improved for cultivating comprehensive skills of students. It is therefore important for us to analyze the correlation between ideological and political teaching and mental health.
Association mining algorithm is an important domain in data mining technology (Padillo, Luna, & Ventura, 2019), especially for analyzing what are interrelationship and interdependence among things at a certain level, for example, it is used for online shopping to analyze the relationship between users and commodities, based on which, the sellers can recommend satisfactory products for users to reap more profit. It is possible to analyze the key factors influencing the ideological and political teaching and mental health using the Apriori algorithm (Belhassena & Wang, 2019). The correlation between the two is then analyzed with the data mining analysis to determine the essential and inherent relations between the two education models. Therefore, it is feasible to provide a new
idea for improving classroom quality and students’
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between ideological and political teaching and mental health with the technical support of Apriori. In view of the above problems in ideological and political and mental health education in universities and discussing the Apriori algorithm in data mining technology, the paper establishes a correlation analysis system for ideological and political teaching and mental health based on the description and analysis of Apriori. Then the Apriori algorithm is applied to the study of association analysis by data preprocessing, association mining and data analysis. The results show that the natures of the two educations present a strong correlation in terms of education objects, ultimate goals and their interactions, having a great enlightening function to improve the classroom teaching quality and cultivate students' comprehensive quality.
APRIORI ASSOCIATION MINING ALGORITHM
Association mining algorithm model
In the study of data mining technology, the association rule is one of the most classic data analysis methods by which the association between things at a certain level can be extracted from data set, thus to find association rule. The whole association mining algorithm model is shown in Figure 1.
Figure 1
.
Model Diagram of Association
Mining Algorithms
As shown in Figure 1, the itemset that frequently appears can be found from the dataset of the things, set the minimum support and confidence, classify the objects in the dataset according to the support size, and find the object greater than the minimum confidence preset. Then, the user can extract the eigen factors according to the association algorithm, and flexibly adjust the association rule based on the preset support and confidence, so that a satisfactory association effect can be rendered.
Description of Apriori algorithm
The core idea of Apriori algorithm is to process the frequent itemset (Liu, Yu, Wei et al., 2017), according to the iterative search method from top to
bottom, that is, the previous set k will be directly iterated into the latter set k+1, in order to extract a certain feature. This process includes the following five steps.
Step 1, search the data set. The support degree in the statistical data set k=1, recorded as L1, and
then based on it, the support degree of k=2 is
counted up, recorded as L2, repeat this action
iteratively, eventually frequent set Lk of k items is
available
Step 2, connecting step. To get frequent set Lkof
k items, the set Ck of Lk-1 and candidate K items
generated by the set itself should be available, then
Ck-1, Ck-2, …, C1 and so on, to ensure that the sets I1
and I1 of items Lk are connected with each other and
then determine whether Ck is an empty set.
Step 3, search and count in the dataset to
calculate the support degree for the set Ck of each
type of candidate k items.
Step 4, pruning step. Assume a set Lk of frequent
k items generated by the set Ck of candidate k items
traverses each object in Ck+1, compare the preset
minimum confidence level and support, those infrequent itemsets are excluded according to the criteria less than the minimum support.
Step 5, iterate k, and skip to Step 2.
The procedure of the above Apriori algorithm can be illustrated in Figure 2.
Figure 2
.
Flow chart of Apriori algorithm
Discover Association Rules Discover
Frequent Itemset Extraction Rule Data Set
User
Start
End Setting minimum
support(k=1)
Scanning Data Set D
Calculate Support
Generate frequent k-item sets(Lk)
Pruning Ck+1
Generate candidate k+1-item sets(Ck+1) Lk is
empty set
Output result
k = k + 1
Y
ASSOCIATION MINING BETWEEN IDEOLOGICAL AND POLITICAL TEACHING AND MENTAL HEALTH 1047
Analysis of Apriori algorithm
In the domain of data mining application, Apriori algorithm is simple and useful, and applied in many areas. However, with further development of scientific research, the fact that the Apriori algorithm itself works inefficiently is highlighted in the following three ways.
(1) there are too many searches for the datasets, and too frequent calls to I/O. It takes heaps of time to cause load pressure on the I/O. To be specific, for
any loop k in the data set, all data in the candidate
set Ck needs to be traversed once in the dataset to
determine whether k+1 is required. If Ck contains
100 data objects, then the entire data set should be traversed 100 times (Luna, Padillo, Pechenizkiy et al., 2018). Thus, in the face of the surge of data sets, this traversal operation is almost disatrous.
(2) There will be huge candidate sets generated
during the iteration. To create Lk+1, the candidate set
Ck must be iteratively created, but it will expand
exponentially in an explosive manner. Assume that the order of magnitudes of the objects in the level 1
frequent itemset is 104, then the order of
magnitudes of the objects in level 2 candidate itemset generated after the iteration of Apriori
algorithm will hit upon 107, consuming more CPU
and memory. Data processing is challenging. (3) Connecting and pruning steps are too cumbersome, and both are almost calculated according to the repeat iterative process, resulting in huge computing capacity, long time-consuming and inefficiency.
In the light of the above gaps of Apriori algorithm, many scholars and experts have developed a variety of methods to improve it, especially those developed with the data mining technology based on sampling, dynamic itemset, matrix, transaction compression and partitioning, all of which have yielded better effects. The paper adopts matrix-based data mining Apriori association mining algorithm, by which excellent effect can be available in terms of execution efficiency and correlation analysis (Trifonov, Korchagin, & Titov, 2018).
ESTABLISHING CORRELATION ANALYSIS
SYSTEM FOR I&P AND MENTAL HEALTH EDUCATION
Analysis of I&P teaching factors
I&P teaching can be analyzed for the following four factors, whether in the traditional or new teaching models.
(1) Teaching attitude. Teachers' attitude towards
ideological and political education determines to what extent they treat this work, and also directly influences students' understanding of political education disciplines. In a sense, it plays an important role in students' active involvement in the classroom (Wang al., 2016).
(2) Teaching model. Influenced by the new
teaching mode, traditional “instilling” teaching
should adopt texts, audios, videos and other diversified modes to teach students and stimulate their interests and enthusiasm. Only in this way can students fundamentally accept the ideological and political education.
(3) Teaching coverage. The teaching coverage of the ideological and political subject basically remains unchanged, but should be added with more factors in the new era. Directed in the new trend of domestic and international development, teaching content should be enriched and expanded in order to meet the individualized demands of students while fulfilling teaching objectives.
(4) Teaching effect. The teaching effect can no longer be assessed based on simple test scores as the sole measure standard. It should be measured by classroom performance, homework assignments, and achievement presentation to present a variety of teaching achievements. In this way, students can be activated to establish correct values.
Analysis of mental health factors
During the transition from high school to college campus life, the most conspicuous change of students is that they have to live away from home and study independently. Relevant survey shows that about 26% of students in universities have psychological problems such as anxiety, bad interpersonal relationship, personality disorder and depression, which in turn greatly influence the students' learning and life. Therefore, considering the source of mental health, the following six factors are involved.
(1) Learning factors. The learning capabilities of students who are able to enter the same university have a little difference from each other. It is very difficult for students to stand out from this newly well-established group. Psychological discrepancy and discomfort will appear if they become mundane students from pre-university excellent ones.
(2) Interpersonal factors. With learning as a core, university life is also an important part of interpersonal communication. It is required to deal with a lot of unacquainted students, teachers, staff. Then those with weak interpersonal skills will have a lot of unnecessary trouble, bringing psychological
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doubt and anxiety on them.
(3) Love factor. Love is an inevitable topic during university, and students will input heaps of time and efforts to develop the relationship with lovers. However badly, the feelings of love are very complicated, great contradiction will generate in this process, thus bringing a lot of pain and negative psychological hints to themselves.
(4) Adaptability factors. When students get access to the campus, they are prone to psychological imbalance in the face of new living environment and learning style, further causing mental dysfunctions (Domino, Gertner, Grabert et al., 2019).
(5) Employment pressure. Nearly two decades of campus life come to an end. To enter a new society to find their own works, the beautiful wishes and the cruel reality form a striking contrast, thus causing a great psychological gap.
(6) Other factors. For example, family calamity and personal safety as other uncontrollable factors make them suffer from psychological problems.
Definition and establishment of correlation analysis system
The paper uses a globally recognized psychometric scale (SCL-90) to assess 100 students including freshmen and seniors from the Beijing Institute of Technology. In the process, there are psychological characteristics in 9 dimensions, i.e. coercion, interpersonal relationships, depression, terror, anxiety, somatization, paranoia, hostility, and psychosis, induced from learning, interpersonal
relationship, love, adaptability, employment
pressure and other factors (Pal & Mitra, 2017). A questionnaire survey was also conducted among 30 in-service teachers from Beijing Institute of Technology from four dimensions: teaching attitude, coverage, method and effect. It is designed to help students establish correct value orientation and develop personality literacy while improving the ideological and political teaching quality.
Therefore, we reconstruct relevant data sets of
“mental health factors” and “Ideological and Political teaching factors” to constitute a new
correlation analysis system applied in data mining. For the mental health factors, partition criterion is underlying to have symptomatic and asymptomatic students; the attribute values are defined as 1 and 0, respectively; for the ideological and political teaching factors, levels 0~4 are used to partition them to indicate the inputs of each factor. After determining partition dimensions and attributes, the code conversion is performed in favor of the
analysis of data mining. At last, the correlation analysis system for ideological and political teaching and mental health is available as shown in Table 1. There are a total of 13 factors, and appropriate data sheets and conversion codes are established.
Table 1.
Systematic Table for the Relevance
Analysis of Ideological and Political Teaching
and Mental Health
Factor Factor value Conversion code
Teaching Attitude 3 TA3 Teaching Content 4 TC4 Teaching Way 3 TW3 Teaching Effect 1 TE1
Compulsion Asymptomatic CA0 Symptomatic CS1 Interpersonal
relationships
Asymptomatic IRA0 Symptomatic IRS1
Depression Asymptomatic DA0 Symptomatic DS1
Terror Asymptomatic TA0 Symptomatic TS1
Anxiety Asymptomatic AA0 Symptomatic AS1
Somatization Asymptomatic SA0 Symptomatic SS1
Paranoia Asymptomatic PA0 Symptomatic PS1
Hostility Asymptomatic HA0 Symptomatic HS1
Mental disease Asymptomatic MDA0 Symptomatic MDS1
Relevant data analysis and processing
13 system factors available in Table 1 are defined as f1, f2, …, f13, then relevant dataset composed of
ideological and political teaching and mental health
forms a 13-dimensional coordinate point,
represented by a set Fi.
(1)
In turn, fi of each factor can be limited to analyze
its correlation with other factors, assume f8=TS1, the
survey dataset g under the fear factor can be
available.
(2)
Similarly, two factors f8 and f9 are limited, a
composite correlation analysis can be available, then:
(3)
With the above simple data processing, the
1 2 3
( ,
,... )
i
F
=
f
f
f
8
( i 1)
i
g=
F f =TS8 9
(
i1
0)
i
ASSOCIATION MINING BETWEEN IDEOLOGICAL AND POLITICAL TEACHING AND MENTAL HEALTH 1049
original data set can be diminished, and then the data mining association algorithm (Canito, Marreiros, & Corchado, 2019) is used to not only obtain the correlation between the factors more quickly but also make the purpose of the study on data scope more definite.
APPLICATION OF APRIORI ALGORITHM IN THE CORRELATION ANALYSIS OF I&P TEACHING AND MENTAL HEALTH
Data preprocessing
As required by the SCL-90 self-assessment scale, the attribute values of factors such as coercion, interpersonal relationship, depression, fear, anxiety, somatization, paranoia, hostility and mental illness are calculated, respectively, and the data structure of psychological factors is established, as shown in Table 2.
Table 2.
Data Structure of Psychological Factors
Factor
name Type
Maximum
length Meaning
CO int 6 Compulsion
IR int 6 Interpersonal relationships DE int 6 Depression TE int 6 Terror AN int 6 Anxiety SO int 6 Somatization PA int 6 Paranoia HO int 6 Hostility MD int 6 Mental disease
Similarly, the data structure of ideological and political teaching factors is established, as shown in Table 3.
Table 3.
Data structure of factors in Ideological
and Political Education
Factor
name Type
Maximum
length Meaning
TA int 10 Teaching Attitude TC int 10 Teaching Content TY int 10 Teaching Way TE int 10 Teaching Effect
Based on the above established data structures, basic data processing should be performed on the association rule mining between the two factors. Combined with the established correlation architecture, based on the principles of the continuous data discretization and the discrete data categorization, purposeful mining and analysis
should be made on the factors (Bhatia, Sharma, & Bhatia, 2018).
Mining process
After data pre-processing, the matrix-based Apriori association mining algorithm is used to perform contextual computing on the I&P teaching and mental health factors to reveal potential connection between them, and define the scopes of support and confidence in correlation analysis (Tzacheva, Sankar, Ramachandran et al., 2017).
In the application of Apriori association mining algorithm, the reduced dataset is used as the mining target, and the jExcelAPI technology can realize the purposeful filter of associated data. The mining attributes are reasonably compiled based on the sizes of the influence factors. After many times of trial computing, data mining and analysis are performed on 13 types of correlation factors, and the results shown in Table 4 are available.
Table 4.
Table of Partial Association Rules
among Thirteen Factors
Association rules Support degree Confidence level
SA0, DA0⟹ MDA0 30% 85% TC4, PS1⟹ IRS1 18% 93% CS1, SS1 ⟹ TA3 22% 88% TA0, AA0⟹ HA0 31% 92% TA3, PS1⟹ TE1 26% 95% PA0, TA3⟹ HA0 29% 82% CS1, SS1⟹ AS1 41% 89% PS1, CS1⟹ TW3 33% 84% HA0, IRA0⟹ MDA0 19% 99% TA3, TW3⟹ SA0 22% 81%
Data analysis
From the results of data mining, we can learn that I&P teaching and mental health factors are highly correlated to each other, and there should be the features of the correlation between all types of factors. Based on the mining results in Table 4, the following correlations can be specifically analyzed:
Interpersonal factors are highly correlated with factors such as anxiety, depression, intimidation and coercion. Poor interpersonal relationship is not conducive to psychologically steady growth, and can easily induce negative mental problems such as anxiety, depression, and coercion which directly lead to poor interpersonal relationships.
There is a high correlation among teaching attitude, effect and sensitivity, somatization and paranoia. Teachers hold a rigorous teaching attitude that helps students face learning and life with open-mindedness, and the good teaching effect is helpful for students' love of learning and life, and more
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proactive and optimistic to face setbacks and difficulties.
There is a highly correlation between students’
sensitivity, hostility, coercion and teaching effects. Students are sensitive and hostile to teachers and classmates in their learning, and they force themselves to participate in ideological and political teaching, which is not conducive to producing good teaching effects.
The above discussion on data mining analysis is only a corner of the analysis of the correlation between ideological and political teaching and mental health. No revolutionary innovative analysis is made from macro and micro angles. However, combined with the physical truth in universities, the law relevant to the two education models can be discussed in the depth from the three dimensions, i.e. education object, ultimate goal and interaction.
(1) Correlation between educational objects. Ideological and political education underlines the subjectivity of human beings, and aims to improve students' ideological consciousness to allow them to realize their social attributes, and culture their traits in terms of emotion, communication and consciousness. Mental health is also people-oriented. It enhances students' mental regulation power while maintaining their psychological traits and physical and mental health. The two education models should be tightly integrated with the people-oriented education object as a contact hub to cultivate students to hold scientific and positive "three views" and help them grow up healthily.
(2) Correlation between the ultimate goals. Ideological and political education aims to train students to adhere to the leadership of the party, arm themselves by mastering the theory of socialism
with Chinese characteristics, to constantly
strengthen moral cultivation, and concern the cultivation and promotion of moral literacy. Mental health can foster the healthy personality, noble morality and good psychology of students, allowing them to face the frustration with positive sunshine attitude and be full of hope for life and future while re-plasticizing their own personalities. The ultimate goals of both education models are to cultivate
students’ sound personalities to be the compound
talents as required in the new era of socialist. (3) Interactions. It can be said that there is interaction between ideological and political and mental health education: ideological and political learning is an important accumulation for mental health education which is in turn the premise of ideological and political education. The two models complement each other, promote each other, and
interact with each other to play a part for enhancing students' ideological and moral education and healthy and optimistic psychology qualities.
CONCLUSION
Using the data mining as the technical support, this paper establishes the correlation analysis system based on the Apriori in association mining algorithms after the factors influencing ideological and political teaching and mental health are analyzed. Eventually, the Apriori is applied to the correlation analysis system. Here discusses the data mining by data preprocessing, association mining and data analysis. The results show that the two present characteristics and laws of interaction in terms of the education object, ultimate goal. It is important to cultivate students to form a sound personality and lofty moral literacy. It is hoped that the study can provide the clues to improving the classroom teaching quality and cultivating students' comprehensive quality.
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