Mathematical Problems in Engineering

Volume 2017 (2017), Article ID 2926904, 12 pages

https://doi.org/10.1155/2017/2926904

## Usability Evaluation Approach of Educational Resources Software Using Mixed Intelligent Optimization

School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China

Correspondence should be addressed to Jiaze Sun

Received 12 July 2017; Revised 11 September 2017; Accepted 26 September 2017; Published 30 October 2017

Academic Editor: José Alfredo Hernández-Pérez

Copyright © 2017 Jiaze Sun. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Aiming at the problems of strong subjectivity and uncertain fuzziness of attribute weights in the software usability evaluation approach, an evaluation approach based on mixed intelligent optimization was proposed, which combines subjective and objective methods to measure software usability for educational resources software. Firstly, the usability evaluation index system of educational resources software was established, and the basic probability assignment was generated by the interval method from the historical sample data. Then the weight optimization problem was adapted to the smooth optimization problem by the maximum entropy function method, and the hybrid social cognitive optimization (HSCO) algorithm was introduced to solve the optimal weights of evidence. Finally, the software usability level was fused by DS evidence theory. The experimental results show that the educational resources software usability evaluation approach can objectively and truly reflect the usability of the software. It provides an efficient way to evaluate the usability of the software.

#### 1. Introduction

In the era of user-centered product design, a good user experience is the direct way to keep the user’s viscosity, for the usability is an important software feature. Software usability evaluation is an important part in the process of software quality assurance. Therefore, usability as product quality evaluation indicator gradually becomes a key factor to be considered in the design of software development.

Software usability evaluation is an important part of software credibility evaluation. Software credibility evaluation is mainly based on the related standard of software quality by the establishment of evaluation model and tool. In the past, the usability of software systems was evaluated subjectively and the process was not well defined, and there was no mature evaluation method that is accepted by most people [1]. Although usability evaluation and analysis methods and methodologies were being developed, software usability evaluation method research is still in its early infancy. At the same time, the usability of software is closely related to the specific applications and users, and software usability evaluation research has all kinds of different methods in different software systems [2].

With the rapid development of the mobile applications, usability of mobile applications is in the focus of the software usability researches. Harrison et al. [3] introduced the PACMAD (People at the Centre of Mobile Application Development) usability mode to solve the limitations of existing usability models when they are applied to mobile applications. Field studies and lab experiments are two methodologies most often applied by researchers; Kaikkonen et al. [4] found that field testing is worthwhile when combining usability tests with a field pilot or contextual study. Chen [5] proposed evaluation method for mobile B2C interface program which is based on adaptation touch interaction and natural gesture interaction and includes usability evaluation index system and analytic hierarchy process (AHP) method for optimizing the weights. Zhang et al. [6] proposed quality indicators system of software products and presented a simple usability evaluation method from the perspective of users and experts. Liu et al. [7] proposed a usability assessment method based on Usability Maturity Model for organizational human-centeredness at some software enterprises, which gives a feasible strategy for introducing usability engineering in the industry. Guo et al. [8] presented a usability evaluation model for application software based on user emotion; however, the evaluation process is entirely subjective assessment that was not easily convincing.

Van Nuland et al. [9] found that usability testing, particularly within the anatomical sciences teaching online, should be employed during the design and development phases, as well as during its delivery. Zhao et al. [10] used fuzzy analytic hierarchy process to quantitatively evaluate software usability, but the result of this method was still greater uncertainty. Li et al. [11] constructed a weighted sum of software usability evaluation method, but the weight of the distribution was not very objective. Li et al. [12] proposed a method of using the AHP method to determine the weight of the weighted DS evidence theory; the initial weight and the initial probability distribution were given by the experts.

Mainly based on subjective evaluation by experts and users, these traditional methods adopt the method of analytic hierarchy process (AHP) and the weighted average to evaluate the software usability. Software usability evaluation problem is ultimately a multiple attribute decision-making problem, which depends on how to establish the availability index system, how to assign attribute weights, and how to fuse the multiple attributes.

DS (Dempster-Shafer) evidence theory uses quantitative and qualitative data to establish the assessment model with great advantage under the unified recognition framework, which helps obtain more accurate results for multiple attribute decision-making (MADM) problems [13]. Literature [14] proposed a hybrid approach to develop the partner evaluation model for tourism partner selection problem, by applying the DS evidence theory and satisfactory principle as alternative framework. Literature [15] proposed a MADM method based on evidential reasoning approach with unknown attribute weights in intuitionistic fuzzy environment. When DS evidence theory is applied for MADM problems, there are two main key problems [16]: basic probability assignment (BPA) generation and attribute weight optimization problems.

At present, there are two kinds of BPA generating methods: expert subjective set method and automatically generating method according to the historical knowledge. Multiple experts independent set methods always have high conflicts. Literature [17] proposed Dynamic Belief Fusion (DBF) method to assign probabilities to individual detectors, which optimally fused information from all detectors. Literature [18] automatically generated BPA which used the history sample data to identify results. Literature [19] generated random numbers based on set theory and presented evidence fusion strategy based on distance. So far, basic probability assignment is no good way to generate. Traditional BPA generation needs complete information to support. But the usability evaluation indexes are different, since software usability metric in different software varies greatly. And because usability testing data are poor and available empirical knowledge is scarce, usability evaluation is very subjective and uncertain. Interval number theory [20] only requires the upper and lower bounds of the information scope, so it is very suitable for the application field in which characteristic information is poor, fuzzy, and imprecise. In literature [21], the basic probability assignment (BPA) is generated based on the distance between interval numbers to improve belief Markov chain model.

To reduce the negative impact of single inaccurate attribute and improve accuracy and stability of the determining system, it is very necessary to fuse the property from multiple aspect sources. But the importance of each attribute in judgment system is different, so the attribute fusion should consider the influence of the weight of the different characteristics. To deal with different weights, the weighted synthesis technology widely uses similarity weighted method and the weighted average method [22]. Literature [23] put forward a kind of evidence synthesis method based on practical experience. But, at present, the DS evidence theory rarely discusses optimization weight acquisition method. The traditional weight acquisition methods mainly have expert subjective weights determining method and history statistical method, which are all difficult to obtain the optimal weight value. Aimed at the shortage of the weighted method in determining the weights of evidence theory, literature [24] uses particle swarm algorithm combining historical data value to obtain the optimal weights in the weighted information fusion problem, but the particle swarm optimization (PSO) algorithm is easy to premature and cannot guarantee the global convergence. Literature [25] proposes a weighted classifier combination method to minimize the distances between fusion results obtained by Dempster’s rule to enhance the classification accuracy.

We apply DS evidence theory to software usability evaluation and establish the software usability evaluation index system in view of the education resources, and a software usability evaluation method based on DS evidence reasoning is proposed. In the new method, the basic probability assignment is produced by using the method of interval number in combination with historical data; the weight optimization problem is transmitted to a smooth optimization problem through maximum entropy function method; the hybrid social cognitive optimization (HSCO) algorithm [26] is adopted to solve the optimal weights problem. As a multiple attribute decision-making problem, software usability evaluation is eventually better solved.

The primary contributions of the paper are as follows:(1)A software usability evaluation index system of the educational resources software based on the ISO/IEC25000 series standards is introduced, which fully considers the characteristics of the educational resources software and the characteristics of the target user. Usability evaluation index system is flexible and is more in line with the needs of educational resources software usability evaluation.(2)The basic probability assignment is generated through the interval number theory from the history of the sample data. Interval number theory is very suitable for poor evaluation information and fuzzy imprecise characteristics.(3)To obtain better fusion effect, the hybrid social cognitive optimization algorithm is used to optimize the different weights of evidence, in which global convergence is guaranteed.

#### 2. Dempster-Shafer Theory

##### 2.1. Basic Conceptions

A belief structure as introduced by Shafer provides an approach to represent nonspecific forms of uncertainty. Formally DS belief structure on space consists of a collection of nonempty crisp subsets of called focal elements: . This represents the value of a variable whose domain is called the frame of discernment [13].

Let stand for a domain set for every possible value of , and every component in is incompatible. And then we call the differentiation frame of ; let stand for the power set of . Let denote the empty set.

*Definition 1. *A basic probability assignment (BPA) is a function , which satisfies the following conditions: (1) and (2) ; is called basic probability number, which represents the proportion of all relevant and available evidence that supports the claim that a particular element of belongs to the set but to no particular subset of .

*Definition 2 (Dempster combination rules). *According to Dempster’s orthogonal rule of evidence combination [13], for basic probability assignment functions in the same frame of discernment , the combination function of is .or here, , indicates that Dempster’s combination rule is used, and are focus elements. is the conflict weight that reflects the information conflict. If the conflict weight is 1, the evidences completely conflict with each other.

##### 2.2. Weighted Transformation

In uncertain information fusion, not all the evidences have the same importance. Some evidences are more important than others. Traditional DS evidence theory does not differentiate the importance of different evidences. To differentiate the importance of different evidences, the weighted value should be processed in the following [27].

Suppose that there are features . For the sake of simplicity, we assume that all features are independent of one another. Their weighted values are . Let .

In formula (3), are the basic probability values of the evidence Fi; denotes the transferred probability assignment function. Through formula (3) transferring, the weight value of the evidence Fi is reflected in the basic probability values of the proposition, which makes the weights of each evidence be transformed equally. Then we can synthesize the basic probability values transformed by DS evidence theory.

#### 3. Hybrid Social Cognitive Optimization (HSCO) Algorithm

By introducing human social intelligence based on social cognitive theory to artificial system, Xie et al. [28] proposed social cognitive optimization (SCO) algorithm in 2002. In SCO optimization procedure, a knowledge library with symbolizing capability consists of many knowledge points and learning agents, which act observational learning via the neighborhood local searching by observing the selected model from tournament selection. Because SCO algorithm fully makes use of the interactions and shares of the entire social swarm, it greatly improves the convergence speed and accuracy of the swarm intelligence algorithm and makes it better than many other well-used intelligent optimization methods, such as Genetic Algorithm (GA), particle swarm optimization (PSO), and Ant Colony Optimization (ACO), in many applications [29].

To improve the global convergence speed and stability of SCO algorithm without increasing the computation, a hybrid social cognitive optimization (HSCO) algorithm, based on elitist strategy and chaotic optimization, was proposed to solve constrained NLPs in literature [26]. Learning agents are partitioned into three groups in proportion: elite learning agents, chaotic learning agents, and common learning agents. The common agents in major proportion work in the search way of traditional SCO. The elite learning agents in a little proportion search via elitist selection to improve the global searching performance. The chaotic learning agents in a little proportion search via chaotic search algorithm to avoid the premature convergence. HSCO algorithm is guaranteed to converge to global optimal solution with probability of one because of the elite learning strategy and the chaotic learning strategy [30].

To select better algorithm to optimize the weight values, we compare HSCO with traditional PSO algorithm in the weight values optimum process in evaluating the usability of the convenient educational resource management platform software (http://222.24.63.99:8080/). The experiments setting is as follows: for HSCO, , , and ; for SCO, the number of particles is also 350. The two algorithms are executed 30 times. We calculate the mean solution by means of having a statistical computation for each run of the HSCO and PSO algorithm.

Figure 1 shows the mean fitness values of 30 times’ iteration, which is performed by PSO and HSCO. The HSCO algorithm shows higher convergence velocity and higher sustainable evolutionary capability at the process of evolution than traditional PSO algorithm. HSCO algorithm can meet the requirement of numerical value in 400 iterations, while the PSO algorithm needs 900 to achieve the same fitness. The HSCO algorithm has higher efficiency in solving weight values optimum problem. Hence, we choose the HSCO algorithm to optimize the weight values in the paper.