Research article Special Issues

The value co-creation behavior in learning communities: Comparing conventional learning and e-learning

  • With the rapid development of ICT, the present world is experiencing rapid changes in the field of education. Implementation of e-learning and ICT in the education system could allow teachers to upgrade and improve their lectures. However, from the perspective of value co-creation behavior in learning communities, conventional learning and e-learning classrooms may encounter different opportunities and challenges. Thus, a more in-depth investigation would be needed. Based on the S-O-R framework, this study identifies self-directed learning as a stimulus, perceived benefits as the organism, and value co-creation behavior as the response. By applying the multi-criteria decision-making techniques of DEMATEL, ANP, and VIKOR, this study explores the causal effects, influential weights, and performance ranking of the primary constructs in the framework as criteria. This study's theoretical and practical implications are discussed, and ways of improving learning performance are suggested.

    Citation: Huan-Ming Chuang, Shahab S. Band, Mehdi Sookhak, Kenneth Pinandhito. The value co-creation behavior in learning communities: Comparing conventional learning and e-learning[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 7239-7268. doi: 10.3934/mbe.2021358

    Related Papers:

    [1] Abdulhamed Alsisi, Raluca Eftimie, Dumitru Trucu . Non-local multiscale approach for the impact of go or grow hypothesis on tumour-viruses interactions. Mathematical Biosciences and Engineering, 2021, 18(5): 5252-5284. doi: 10.3934/mbe.2021267
    [2] Marco Scianna, Luigi Preziosi, Katarina Wolf . A Cellular Potts model simulating cell migration on and in matrix environments. Mathematical Biosciences and Engineering, 2013, 10(1): 235-261. doi: 10.3934/mbe.2013.10.235
    [3] Thomas Hillen, Kevin J. Painter, Amanda C. Swan, Albert D. Murtha . Moments of von mises and fisher distributions and applications. Mathematical Biosciences and Engineering, 2017, 14(3): 673-694. doi: 10.3934/mbe.2017038
    [4] Benjamin Steinberg, Yuqing Wang, Huaxiong Huang, Robert M. Miura . Spatial Buffering Mechanism: Mathematical Model and Computer Simulations. Mathematical Biosciences and Engineering, 2005, 2(4): 675-702. doi: 10.3934/mbe.2005.2.675
    [5] Yuelin Yuan, Fei Li, Jialiang Chen, Yu Wang, Kai Liu . An improved Kalman filter algorithm for tightly GNSS/INS integrated navigation system. Mathematical Biosciences and Engineering, 2024, 21(1): 963-983. doi: 10.3934/mbe.2024040
    [6] Abdulhamed Alsisi, Raluca Eftimie, Dumitru Trucu . Nonlocal multiscale modelling of tumour-oncolytic viruses interactions within a heterogeneous fibrous/non-fibrous extracellular matrix. Mathematical Biosciences and Engineering, 2022, 19(6): 6157-6185. doi: 10.3934/mbe.2022288
    [7] Alessandro Bertuzzi, Antonio Fasano, Alberto Gandolfi, Carmela Sinisgalli . Interstitial Pressure And Fluid Motion In Tumor Cords. Mathematical Biosciences and Engineering, 2005, 2(3): 445-460. doi: 10.3934/mbe.2005.2.445
    [8] Chayu Yang, Jin Wang . Computation of the basic reproduction numbers for reaction-diffusion epidemic models. Mathematical Biosciences and Engineering, 2023, 20(8): 15201-15218. doi: 10.3934/mbe.2023680
    [9] Nalin Fonseka, Jerome Goddard Ⅱ, Alketa Henderson, Dustin Nichols, Ratnasingham Shivaji . Modeling effects of matrix heterogeneity on population persistence at the patch-level. Mathematical Biosciences and Engineering, 2022, 19(12): 13675-13709. doi: 10.3934/mbe.2022638
    [10] Aníbal Coronel, Fernando Huancas, Ian Hess, Alex Tello . The diffusion identification in a SIS reaction-diffusion system. Mathematical Biosciences and Engineering, 2024, 21(1): 562-581. doi: 10.3934/mbe.2024024
  • With the rapid development of ICT, the present world is experiencing rapid changes in the field of education. Implementation of e-learning and ICT in the education system could allow teachers to upgrade and improve their lectures. However, from the perspective of value co-creation behavior in learning communities, conventional learning and e-learning classrooms may encounter different opportunities and challenges. Thus, a more in-depth investigation would be needed. Based on the S-O-R framework, this study identifies self-directed learning as a stimulus, perceived benefits as the organism, and value co-creation behavior as the response. By applying the multi-criteria decision-making techniques of DEMATEL, ANP, and VIKOR, this study explores the causal effects, influential weights, and performance ranking of the primary constructs in the framework as criteria. This study's theoretical and practical implications are discussed, and ways of improving learning performance are suggested.



    Diffusion in complex media is a perennial research topic, whose underlying model is a partial differential equation (PDE) of parabolic type. Our motivation comes from the simulation of water diffusion inside the brain tissue (white and grey matter) problem in medical sciences [1].

    Simulation of the water diffusion inside the brain tissue is motivated by the analysis of the diffusion magnetic resonance images (dMRI). The diffusion simulation is performed using Monte Carlo methods [2]. This study provides insightful novel theories on the diffusion properties with analytical representations [3] and generates useful data for the validation of model fitting [4]. In this problem, the domain is composed of cell bodies (soma, axons, neurites, glial cells, vessels, etc). Despite some of the cell structures can be approximated with simple geometries, the domains are geometrically complex. In particular, the extracellular spaces present arbitrary shapes (similar to a "gruyere-cheese shape"), hence numerical approximations are required to simulate the molecular diffusivity.

    The approximation of the extracellular diffusivity profile on a disordered model of cylindrical brain axons, with the diameters computed from a Gamma distribution, is the focus of this study [2]. The distribution of diameters has been empirically observed in histological studies on ex vivo samples of the brain [5].

    The extracellular diffusion process is characterized by the corresponding 3D covariance matrix Σ [6] of a Gaussian Ensemble Average Propagator, i.e., the Spatio-temporal displacement distribution of the water molecules P(x,t) [7]. The Σ eigenvalues (matched with the corresponding eigenvectors) indicate the magnitude and orientational dependency, such that, it is possible to infer extracellular microstructure features like the main orientation of the axon bundles, percentage of the volume occupied by neurons (intracellular signal fraction), amount of diffusion anisotropy of the tissue (fractional anisotropy), and axial and radial diffusivity. Those descriptors computed from Σ have been correlated with several brain damage and diseases [1].

    In the Diffusion Weighted Magnetic Resonance (DWMR) medical literature, the covariance matrix is computed using the zeppellin model from the extracellular MR signal, i.e., a limited diffusion tensor model with two equal eigenvalues [6,8]. The estimated Σ is also used to compute synthetic MR signals when the Short-Gradient-Pulse approximation is assumed [7].

    The approximation of the covariance matrix is essential to correlate its properties to the microstructural features and their change, respectively, due to, tissue damages associated with brain diseases [1]. The covariance matrix can be obtained from a Gaussian density, which is the average density resulting from the solution of diffusion equations. We contribute by solving these diffusion equations using an ad hoc implementation of the Discontinuous Galerkin (DG) method [9].

    In applications, the numerical solution of the PDE is part of the research process. This is performed in tandem, a model is first proposed, then a numerical method for simulation. This work applies the DG method to accomplish both objectives simultaneously.

    Our implementation takes advantage of the underlying physical and geometric properties of the problem as posed by the clinicians. The so-called substrates are squared pixel domains that allow a uniform mesh. Also, we suppose that no diffusivity occurs between the axons and the extracellular region (impermeability) [6]. The usual approximation solves the diffusion equation in the extracellular region imposing a zero Neumann condition. The numerical fluxes then model the interaction between the axon walls and the extracellular region. This interaction corresponds to solving the diffusion equation in the whole domain. Thus, null computations are performed in axons. This redundant strategy not only circumvents the computationally expensive task, in Galerkin-type methods, of handling the boundary conditions, but more importantly, it allows the full strength of the DG method, to conduct the time update of the solution in parallel for all elements. A small ODE problem is solved in each element with no communication. Consequently, a GPU-CUDA implementation is appropriate.

    The actual problem of water diffusion inside the brain tissue is in 3D. We highlight the modeling and computational features of the DG method. We present computational results only in 2D, but our theoretical framework will be in 3D. The complex extracellular 2D domains used for our simulations are from the assumption that intracellular spaces (axons) can be modeled as cylinders such that the diffusion parallel to the main axon bundle orientation is unhindered, while the study focuses on the estimation of the diffusion properties perpendicular to the axons [10]. The aforementioned assumption is a well-known approximation when the axon pack density is high, as in the brain's corpus callosum region, and employed in various studies [3,4]. The outline is as follows:

    Section 2 introduces the initial boundary value problem (IVBP) of the diffusion equation associated with the phenomenon. Next, the basics of the DG method are discussed to present the numerical fluxes modeling the interaction between the axons and the intercellular region. Then, the computational model for GPU-CUDA implementation is described. We complete the methodology with the scheme for computing the covariance matrix. Section 3 provides two numerical examples of the proposed method. First, the analytical solution of a free diffusion problem is considered to gauge the introduced DG-CUDA method and a classical Monte Carlo approach. Second, a benchmark type substrate associated with an ex vivo experiment is introduced to test the computational methodology. A computational comparison using the execution time is also described. We close with conclusions and a brief discussion on future work in Section 4.

    We model a substrate occupying a domain (medium) comprised of two regions, the axons, and extracellular complement. The former is nondiffusive and the latter a diffusive region. We assume that the boundary between both regions is reflecting. Initial pulses are prescribed in the extracellular region, far away from the outer boundary which is modeled as a perfect absorber.

    Let Ω be the domain occupied by the substrate. This domain is the union of two intertwined adjacent regions, Ωa and Ωe representing the axon and extracellular regions, respectively.

    The IVBP consists of finding u that solves the diffusion equation

    ut=(k(x)u),(x,t)Ωe×(0,T), (2.1)

    given Cauchy data

    u(x,0)=u0(x),xΩe, (2.2)

    and boundary values

    un=0,(x,t)Ωe×(0,T), (2.3)
    u(x,t)=0,(x,t)Ω×(0,T). (2.4)

    Here n is the outer unit normal to Ωe.

    The Neumann boundary condition (2.3) corresponds to a reflecting boundary, whereas the Dirichlet boundary condition (2.4) to that of a perfect barrier.

    The discontinuous diffusion is given by

    k(x)={0,xΩak0,xΩe (2.5)

    for a positive constant k0.

    Let Ωe be partitioned into nonoverlapping polygonal elements, e.g. a triangulation. Denote by KK, one of such elements, see Figure 1.

    Figure 1.  Integration element and its normal.

    A striking characteristic of the DG method is that it reduces the PDE to a first-order system. Consequently, consider

    {q=uut=(kq). (2.6)

    Multiplying (2.6) by the test functions v=(v1,v2), and v, we obtain after integrating by parts,

    Kqvdx=K(u)vdx=Kv(un)dsKuvdx
    Kutvdx=K(kq)vdx=Kv(kqn)dsKkqvdx

    Continuity is not enforced at the boundary of adjacent elements. Therefore, the boundary terms u, kq are replaced with boundary numerical fluxes uu(u,u+), (kq)(kq)(k,q,k+,q+). As customary, the superscript denotes limits from the interior of K, and the + superscript, limits from the exterior.

    This yields in element K

    Kqvdx=Kv(un)dsKuvdxKutvdx=Kv(kq)ndsKkqvdx (2.7)

    These numerical fluxes chosen below model the underlying physical phenomena.

    Another characteristic of the DG method, is the element-wise approximation of the unknown q and u.

    Suppose w is an approximation of any scalar functions q1, q2, u. Then, the approximation within K is given by

    w(x,t)=pj=0wj(t)Nj(x). (2.8)

    For a triangulation, choosing the functions Nj follows the Finite Element Method with Lagrangian interpolation.

    We assume that u+ and q+ are known in (2.7). Hence, we solve a differential-algebraic system for u and q. The solution in time is advanced by a Runge-Kutta method.

    Remark. The solution of the p+1 differential-algebraic system (2.7), is solved independently for each element. In practice p3 suffices. Thus, we have small systems, that do not exchange information on each time step, to solve.

    No preferred direction of propagation in diffusion phenomena exists, thus for u, a central flux is considered, namely,

    u=u+u+2.

    A physical assumption is that there is no flow between axons and the extracellular region. Consequently, for q, we propose the numerical flow

    (kq)=2kk+(k)2+(k+)212(q+q+).

    This is coined for the problem under consideration. If k>0 and k+=0, the harmonic mean forces a zero Neumann condition, hence there is no flow trough the boundary of the element K as required.

    The meshing is time-consuming in Galerkin-type methods, as it is boundary conditions handling when assembling the local systems. Here, the domain is divided into square pixels. For meshing, we consider the simplest triangular mesh by separating these squares through the diagonals.

    Also in this MRI application, the heat equation is solved in the extracellular region Ωe with Cauchy conditions. No consideration is given to the axon region, Ωa. Nevertheless, we solve the heat equation in elements contained in Ωa where the contribution to the solution is null.

    We balance computations on every element. From the remark above, the calculations in (2.7) are element-independent when updating the time. Consequently, the main ingredients for parallel processing using GPUs are met and include small balanced computations with no exchange of information between processors.

    Let (xi,yi)Ωe, i=1,2,,m randomly and uniformly in Ωe. Set a final time T.

    ● For i=1,2,,m, let Ui(x,y,T) be the solution of the IVBP (2.1)(2.4), where the Cauchy condition is the Dirac delta function supported in (xi,yi).

    ● For i=1,2,,m, let ui(x,y,T) be obtained from Ui(x,y,T) by centering and normalization to yield a density function.

    ● Construct the mixture model given as

    u(x,y)=1mmi=1ui(x,y,T).

    ● Fit a Gaussian density to u. That is, determine a covariance matrix Σ, such that

    u(x,y;Σ)12π|Σ|exp(12[((x,y)(μx,μy))TΣ1((x,y)(μx,μy))]),

    The covariance matrix is given by,

    Σ=(E[(Xμx)(Xμx)]E[(Xμx)(Yμy)]E[(Yμy)(Xμx)]E[(Yμy)(Yμy)]).

    It is computed by quadrature rules using uij, the values of the numerical solution at the nodes (xi,yj) in the mesh.

    In this example, we solve the heat equation with the Cauchy condition as the Dirac delta supported at the origin. The uniform diffusion coefficient is k0=450μm/s2.

    We compare the free diffusion approximation (without axons), versus the analytical solution. The latter is a bivariate Gaussian function f(x,y;Σ) defined in (2.4), and the covariance matrix is

    Σ=(2Tk002Tk).

    Taking k=450μm/s2 and T=0.036s, the non-zero coefficients in the covariance matrix are equal to 32.4.

    Graphical results are shown in Figure 2. The DG-CUDA Gaussian matrix fit for 400×400 is

    (32.530.000380.0003832.53)
    Figure 2.  Left: DG-CUDA free diffusion. Right: 1D view.

    An alternative construction of the covariance matrix is using Monte Carlo diffusion simulation [2]. We give the list of the corresponding data in their notation.

    Diffusion constant D=4.5e10, ts=0.036 duration of the diffusion simulation. T=5000 is the number of time steps in the simulation. The step length l is obtained using the relation

    l=4DtsT0.11μm.

    The obtained MC Gaussian matrix is

    (32.4873583290.0758899400.07588994032.378282498)

    Remark. To gauge the approximations we compute the least squares residual

    ij[u(xi,yi;Σ)uij]2. (3.1)

    In both cases, the approximation of the Gaussian function is accurate. The least squares residual (3.1) is of the order O(108). In practice, the Gaussian density functions coincide. However, the DG-CUDA approximation is structurally more consistent. The matrix is symmetric, the diagonal values coincide and the other terms are two orders of magnitude smaller than those obtained from MC.

    We consider a substrate consisting of 1901 nonoverlapping circles, which represent the axons, and consider the regions only within a square, the domain Ω, that measures 50μm on the side (Figure 3). The radius of the circles ranges from 0.150μm to 1.141μm. The extracellular region is the exterior of the circles and the diffusion coefficient is set to k0=450μm/s2.

    Figure 3.  The substrate consists of 1901 nonoverlapping circles.

    The ex vivo coefficient oscillates between 450 and 600, depending on the temperature and substance of the medium [11]. The smaller substrate is used.

    The substrate under study occupies a square domain of side 50μm. Cauchy condition is given within a centered box of side 20μm and observation time t=0.036s. At this given time, the outer boundary is not reached by diffusion. In all cases, a square grid of K×L400×400 mesh is used.

    The axon region Ωa is defined by 1901 axons. The diffusion coefficient as in (2.5). The scheme above is applied to a mixture of m=37 densities. We show one PDE solution in Figure 4.

    Figure 4.  Left: One PDE solution. Right: Normalized and centered solution for one PDE.

    The covariance matrix by the DG-CUDA algorithm is

    (19.500.00880.008819.50)

    and the corresponden one obtained MC Gaussian matrix is

    (19.590.0270.02719.50)

    where the relative error between entries is smaller than 0.5%.

    Remark. The choice of the sample size, m, for the Gaussian mixture is heuristic. From our experiments, m need not be large. In two dimensions O(10) will suffice.

    We summarize the execution time in the following table

    K×L Time stps No. Deltas CPU GPU
    400× 400 5184 1 6585.47 sec. 5.13649 sec.
    400 × 400 5184 37 21877.252 sec. 188.597 sec.

     | Show Table
    DownLoad: CSV

    Regardless of the diffusion coefficient, the GPU processing is more than 1000 times faster for the solution of the heat equation with a single delta as Cauchy condition.

    For the prototype computation, we implement parallel processing using GPUs in the CUDA language of a DELL laptop with hardware:

    CPU: Intel(R) Core(TM) i7-6820HQ CPU @ 2.70 GHz

    GPU: NVIDIA Corporation GM107GLM [Quadro M1000M]

    This study proposed a methodology for the efficient computation of the covariance matrix associated with the extracellular diffusivity profile on a disordered model of cylindrical brain axons. The methodology relies on two strategies; introducing physical numerical fluxes in the Discontinuous Galerkin method and null computations on the axon region for straightforward GPU implementation.

    We present satisfactory results on two-dimensional examples that show the proposed methodology outperforms sequential implementations and is more structurally consistent than a Monte Carlo approach.

    Presently, we are working toward extending our methodology to the three-dimensional case. We contend that our methodology can include the corresponding complexities in a 3D extension. We mention that the choice of the sample size, m, for the Gaussian mixture is heuristic and as such, an in-depth analysis is required. These issues make up our current and future work.

    M. A. Moreles acknowledges partial support from the project CONACYT A1-S-17634. The authors would like to thank Enago (www.enago.com) for the English language review.

    All authors declare no conflicts of interest in this paper.



    [1] N. Ahmad, N. Quadri, M. Qureshi, M. Alam, Relationship modeling of critical success factors for enhancing sustainability and performance in e-learning, Sustainability, 10 (2018), 4776. doi: 10.3390/su10124776
    [2] V. Nikolic, D. Petkovic, N. Denic, M. Milovancevic, S. Gavrilovic, Appraisal and review of e-learning and ICT systems in teaching process, Phys. A., 513 (2019), 456-464. doi: 10.1016/j.physa.2018.09.003
    [3] A. Corbett, H. Trask, Instructional interventions in computer-based tutoring: differential impact on learning time and accuracy, in Conference on Human Factors in Computing Systems, (2000), 97-104.
    [4] S. Kala, S. A. Isaramalai, A. Pohthong, Electronic learning and constructivism: a model for nursing education, Nurse Educ. Today, 30 (2010), 61-66. doi: 10.1016/j.nedt.2009.06.002
    [5] A. Voutilainen, T. Saaranen, M. Sormunen, Conventional vs. e-learning in nursing education: A systematic review and meta-analysis, Nurse Educ. Today, 50 (2017), 97-103. doi: 10.1016/j.nedt.2016.12.020
    [6] T. Y. Lee, F. Y. Lin, The effectiveness of an e-learning program on pediatric medication safety for undergraduate students: a pretest-post-test intervention study, Nurse Educ. Today, 33 (2013), 378-383. doi: 10.1016/j.nedt.2013.01.023
    [7] R. V. D. Gonzalez, T. M. de Melo, The effects of organization context on knowledge exploration and exploitation, J. Bus Res., 90 (2018), 215-225.
    [8] H. Liu, D. Song, Z. Cai, Knowledge management capability and firm performance: the mediating role of organizational agility, in PACIS, (2014).
    [9] T. Gammill, M. Newman, Factors associated with faculty use of web-based instruction in higher education, J. Agric. Educ., 46 (2005), 60-71. doi: 10.5032/jae.2005.04060
    [10] A. Mehrabian, J. A. Russell, An approach to environmental psychology, in An approach to environmental psychology, The MIT Press, (1974), 266.
    [11] Y. H. Fang, Does online interactivity matter? exploring the role of interactivity strategies in consumer decision making, Comput. Human Behav., 28 (2012), 1790-1804. doi: 10.1016/j.chb.2012.04.019
    [12] P. Mohammadi, S. M. Araghi, The relationship between learners' self-directed learning readiness and their English for specific purposes course accomplishment at distance education in Iran, SiSAL J., 4 (2013), 73-84.
    [13] M. S. Knowles, Self-directed learning: a guide for learners and teachers, Group Organ Stud., 2 (1977), 256-257. doi: 10.1177/105960117700200220
    [14] A. S. Andersen, S. B. Heilesen, The problem-oriented project work (PPL) alternative in self-directed higher education, in Inquiry-based learning for multidisciplinary programs: A conceptual and practical resource for educators, Emerald Group Publishing Limited, (2015).
    [15] L. Song, E. S. Singleton, J. R. Hill, M. H. Koh, Improving online learning: Student perceptions of useful and challenging characteristics, Internet High Educ., 7 (2004), 59-70. doi: 10.1016/j.iheduc.2003.11.003
    [16] B. Lin, C. T. Hsieh, Web-based teaching and learner control: a research review, Comput Educ., 37 (2001), 377-386. doi: 10.1016/S0360-1315(01)00060-4
    [17] Y. C. Hsu, Y. M. Shiue, The effect of self-directed learning readiness on achievement comparing face to face and two way distance learning instruction, Int. J. Instr. Media, 32 (2005), 143.
    [18] M. L. Hung, C. Chou, C. H. Chen, Z. Y. Own, Learner readiness for online learning: Scale development and student perceptions, Comput Educ., 55 (2010), 1080-1090. doi: 10.1016/j.compedu.2010.05.004
    [19] A. Biemiller, D. Meichenbaum, The nature and nurture of the self-directed learner, in The Evolution of Cognitive Behavior Therapy, Taylor & Francis Group, (2017).
    [20] L. Lumsden, Student motivation: Cultivating a love of learning, in ERIC Clearinghouse on Educational Management, ERIC Publications, (1999).
    [21] R. Renchler, Student motivation, school culture, and academic achievement: What school leaders can do, in Trends and Issues, Eric Clearinghouse on Educational Management, (1992).
    [22] J. Hewitt Taylor, Self-directed learning: Views of teachers and students, J. Adv. Nurs., 36 (2001), 496-504.
    [23] S. Loyens, J. Magda, R. Rikers, Self-directed learning in problem-based learning and its relationships with self-regulated learning, Educ. Psychol. Rev., 20 (2008), 411-427. doi: 10.1007/s10648-008-9082-7
    [24] L. M. Guglielmino, Development of the self-directed learning readiness scale, University of Georgia, (1977).
    [25] S. D. Brookfield, The getting of wisdom: what critically reflective teaching is and why it's important., in Becoming a critically reflective teacher, Wiley, (1995).
    [26] J. D. Stiller, R. Ryan, Teachers, parents, and student motivation: the effects of involvement and autonomy support, in the Annual Meeting of the American Educational Research Association, (1992).
    [27] M. Gibbons, Challenging adolescent students to excel, in The Self-Directed Learning Handbook, Wiley, (2003).
    [28] A. Smedley, The self-directed learning readiness of beginning bachelor of nursing students, J. Res. Nurs., 12 (2007), 373-385. doi: 10.1177/1744987107077532
    [29] T. Teo, et al., The self-directed learning with technology scale (SDLTS) for young students: an initial development and validation, Comput. Educ., 55 (2010), 1764-1771.
    [30] E. U. Avdal, The effect of self-directed learning abilities of student nurses on success in Turkey, Nurse Educ. Today., 33 (2013), 838-841. doi: 10.1016/j.nedt.2012.02.006
    [31] S. F. Cheng, C. L. Kuo, K. C. Lin, J. Lee Hsieh, Development and preliminary testing of a self-rating instrument to measure self-directed learning ability of nursing students, Int. J. Nurs. Stud., 47 (2010), 1152-1158.
    [32] E. Deci, R. Ryan, Conceptualizations of intrinsic motivation and self-determination, in Intrinsic motivation and self-determination in human behavior, Springer, (1985), 11-40.
    [33] A. Fairchild, K. Barron, S. J. Finney, S. Horst, Evaluating new and existing validity evidence for the academic motivation scale, Contemp. Educ. Psychol., 30 (2005), 331-358. doi: 10.1016/j.cedpsych.2004.11.001
    [34] N. Annuar, R. Shaari, The antecedents toward self-directed learning among distance learner in Malaysian public universities, in Proceeding of the Global Summit on Education, (2014), 195-205.
    [35] W. E. Scott Jr, J. L. Farh, P. M Podsakoff, The effects of "intrinsic" and "extrinsic" reinforcement contingencies on task behavior, Organ. Behav. Hum. Decis. Process., 41 (1988), 405-425.
    [36] A. W. Wood, Groundwork for the metaphysics of morals, in Grounding for the Metaphysics of Morals, Yale University Press, (2002).
    [37] M. K. O. Lee, C. M. K. Cheung, Z. Chen, Acceptance of internet-based learning medium: the role of extrinsic and intrinsic motivation, Inf. Manag., 42 (2005), 1095-1104. doi: 10.1016/j.im.2003.10.007
    [38] N. Entwistle, Motivation and approaches to learning: motivating and conceptions of teaching, in Motivating students, Routledge, (2019).
    [39] M. A. Maras, J. W. Splett, W. M. Reinke, M. Stormont, K. C. Herman, School practitioners' perspectives on planning, implementing, and evaluating evidence-based practices, Child Youth Serv. Rev., 47 (2014), 314-322.
    [40] R. Kim, L. Olfman, T. Ryan, E. Eryilmaz, Leveraging a personalized system to improve self-directed learning in online educational environments, Comput. Educ., 70 (2014), 150-160. doi: 10.1016/j.compedu.2013.08.006
    [41] A. Krause, A. North, B. Heritage, The uses and gratifications of using Facebook music listening applications, Comput. Human Behav., 39 (2014), 71-77. doi: 10.1016/j.chb.2014.07.001
    [42] I. Reychav, D. Wu, Exploring mobile tablet training for road safety: A uses and gratifications perspective, Comput. Educ., 71 (2014), 43-55. doi: 10.1016/j.compedu.2013.09.005
    [43] A. Y. K. Chua, D. H. L. Goh, C. S. Lee, Mobile content contribution and retrieval: An exploratory study using the uses and gratifications paradigm, Inf. Process. Manag., 48 (2012), 13-22. doi: 10.1016/j.ipm.2011.04.002
    [44] M. Grellhesl, N. Punyanunt Carter, Using the uses and gratifications theory to understand gratifications sought through text messaging practices of male and female undergraduate students, Comput. Human Behav., 28 (2012), 2175-2181.
    [45] H. Ko, C. H. Cho, M. S. Roberts, Internet uses and gratifications: a structural equation model of interactive advertising, J. Advert., 34 (2005), 57-70. doi: 10.1080/00913367.2005.10639191
    [46] T. E. Ruggiero, Uses and gratifications theory in the 21st century, Mass Commun. Soc., 3 (2000), 3-37. doi: 10.1207/S15327825MCS0301_02
    [47] J. G. Blumler, E. Katz, Current perspectives on gratifications research, in The Uses of Mass Communications, Sage Publications, (1974), 318
    [48] S. Nambisan, R. Baron, Virtual customer environments: testing a model of voluntary participation in value co-creation activities, J. Prod. Innov. Manage., 26 (2009), 388-406. doi: 10.1111/j.1540-5885.2009.00667.x
    [49] P. Palmgreen, Uses and gratifications: a theoretical perspective, Ann. Int. Commun. Assoc., 8 (1984), 20-55.
    [50] S. Veenman, N. van Benthum, D. Bootsma, J. van Dieren, N. van der Kemp, Cooperative learning and teacher education, Teach. Educ., 18 (2002), 87-103.
    [51] Y. Yi, T. Gong, The effects of customer justice perception and affect on customer citizenship behavior and customer dysfunctional behavior, Ind. Mark. Manag., 37 (2008), 767-783. doi: 10.1016/j.indmarman.2008.01.005
    [52] Y. Yi, T. Gong, Customer value co-creation behavior: scale development and validation, J. Bus Res., 66 (2013), 1279-1284. doi: 10.1016/j.jbusres.2012.02.026
    [53] Y. Yi, R. Nataraajan, T. Gong, Customer participation and citizenship behavioral influences on employee performance, satisfaction, commitment, and turnover intention, J. Bus Res., 64 (2011), 87-95.
    [54] J. D. Johnson, Cancer-related information seeking, in Health Commun., Hampton Press, (1997).
    [55] C. T. Ennew, M. R. Binks, Impact of participative service relationships on quality, satisfaction and retention: an exploratory study, J. Bus Res., 46 (1999), 121-132.
    [56] N. Koriat, R. Gelbard, Knowledge sharing motivation among IT personnel: integrated model and implications of employment contracts, Int. J. Inf. Manag., 34 (2014), 577-591. doi: 10.1016/j.ijinfomgt.2014.04.009
    [57] M. Groth, D. P Mertens, R. Murphy, Customers as good soldiers: examining citizenship behaviors in internet service deliveries, J. Manag., 31 (2005), 7-27.
    [58] M. S. Rosenbaum, C. A. Massiah, When customers receive support from other customers: exploring the influence of intercustomer social support on customer voluntary performance, J. Serv. Res., 9 (2007), 257-270. doi: 10.1177/1094670506295851
    [59] R. Algesheimer, U. M. Dholakia, A. Herrmann, The social influence of brand community: evidence from european car clubs, J. Mark., 69 (2005), 19-34. doi: 10.1509/jmkg.69.3.19.66363
    [60] W. W. Wu, Y. T. Lee, Developing global managers' competencies using the fuzzy DEMATEL method, Expert Syst. Appl., 32 (2007), 499-507. doi: 10.1016/j.eswa.2005.12.005
    [61] W. W. Wu, Choosing knowledge management strategies by using a combined ANP and DEMATEL approach, Expert Syst. Appl., 35 (2008), 828-835. doi: 10.1016/j.eswa.2007.07.025
    [62] T. L. Saaty, Decision making with the analytic hierarchy process, Int. J. Serv. Sci., 1 (2008), 83-98.
    [63] C. L. Chang, C. H. Hsu, Multi-criteria analysis via the VIKOR method for prioritizing land-use restraint strategies in the Tseng-Wen reservoir watershed, J. Environ. Manag., 90 (2009), 3226-3230. doi: 10.1016/j.jenvman.2009.04.020
    [64] S. Opricovic, G. H. Tzeng, Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS, Eur. J. Oper. Res., 156 (2004), 445-455. doi: 10.1016/S0377-2217(03)00020-1
    [65] A. L. Hyde, D. E. Conroy, A. L. Pincus, N. Ram, Unpacking the feel-good effect of free-time physical activity: between-and within-person associations with pleasant-activated feeling states, J. Sport Exerc. Psychol., 33 (2011), 884-902. doi: 10.1123/jsep.33.6.884
    [66] A. Strauss, J. Corbin, Techniques and procedures for developing grounded theory, in Basics of Qualitative Research, Sage publications, (1998).
    [67] N. Northcutt, D. McCoy, A systems method for qualitative research, in Interactive qualitative analysis, Sage Publications, (2004).
    [68] W. Y. Chiu, G. H. Tzeng, H. L. Li, A new hybrid MCDM model combining DANP with VIKOR to improve e-store business, Knowl-Based Syst., 37 (2013), 48-61. doi: 10.1016/j.knosys.2012.06.017
    [69] K. Y. Shen, M. R. Yan, G. H. Tzeng, Combining VIKOR-DANP model for glamor stock selection and stock performance improvement, Knowl-Based Syst., 58 (2014), 86-97. doi: 10.1016/j.knosys.2013.07.023
    [70] L. Cadorin, G. Bortoluzzi, A. Palese, The self-rating scale of self-directed learning (SRSSDL): a factor analysis of the Italian version, Nurse Educ. Today, 33 (2013), 1511-1516. doi: 10.1016/j.nedt.2013.04.010
    [71] D. Kim, M. Yoon, I. H. Jo, R. Maribe Branch, Learning analytics to support self-regulated learning in asynchronous online courses: a case study at a women's university in South Korea, Comput. Educ., 127 (2018), 233-251.
    [72] K. Li, MOOC learners' demographics, self-regulated learning strategy, perceived learning and satisfaction: a structural equation modeling approach, Comput. Educ., 132 (2019), 16-30.
    [73] N. Lung Guang, Decision-making determinants of students participating in MOOCs: merging the theory of planned behavior and self-regulated learning model, Comput. Educ., 134 (2019), 50-62.
    [74] L. Song, J. Hill, A conceptual model for understanding self-directed learning in online environments, J. Interact. Online Learn., 6 (2007), 27-42.
    [75] W. Chow, L. Sheung Chan, Social network, social trust and shared goals in organizational knowledge sharing, Inf. Manag., 45 (2008), 458-465.
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3649) PDF downloads(159) Cited by(1)

Figures and Tables

Figures(5)  /  Tables(12)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog